Overview

Dataset statistics

Number of variables39
Number of observations3550
Missing cells43937
Missing cells (%)31.7%
Duplicate rows109
Duplicate rows (%)3.1%
Total size in memory1.1 MiB
Average record size in memory312.0 B

Variable types

Categorical19
Numeric20

Warnings

Dataset has 109 (3.1%) duplicate rows Duplicates
author_id has a high cardinality: 2412 distinct values High cardinality
description has a high cardinality: 3400 distinct values High cardinality
bookedition has a high cardinality: 68 distinct values High cardinality
published_date has a high cardinality: 278 distinct values High cardinality
publisher_id has a high cardinality: 257 distinct values High cardinality
lexile_measure has a high cardinality: 61 distinct values High cardinality
genre_0 has a high cardinality: 80 distinct values High cardinality
genre_1 has a high cardinality: 123 distinct values High cardinality
genre_2 has a high cardinality: 199 distinct values High cardinality
genre_3 has a high cardinality: 233 distinct values High cardinality
genre_4 has a high cardinality: 261 distinct values High cardinality
genre_5 has a high cardinality: 287 distinct values High cardinality
genre_6 has a high cardinality: 299 distinct values High cardinality
genre_7 has a high cardinality: 325 distinct values High cardinality
genre_8 has a high cardinality: 343 distinct values High cardinality
genre_9 has a high cardinality: 349 distinct values High cardinality
description has 38 (1.1%) missing values Missing
bookedition has 3319 (93.5%) missing values Missing
pages has 99 (2.8%) missing values Missing
published_date has 2982 (84.0%) missing values Missing
publisher_id has 2982 (84.0%) missing values Missing
reading_age has 3424 (96.5%) missing values Missing
lexile_measure has 3462 (97.5%) missing values Missing
grade_level has 3450 (97.2%) missing values Missing
weight has 3031 (85.4%) missing values Missing
rating_value_1 has 2997 (84.4%) missing values Missing
dimension_0 has 3038 (85.6%) missing values Missing
dimension_1 has 3038 (85.6%) missing values Missing
dimension_2 has 3051 (85.9%) missing values Missing
genre_0 has 150 (4.2%) missing values Missing
genre_1 has 197 (5.5%) missing values Missing
genre_2 has 228 (6.4%) missing values Missing
genre_3 has 248 (7.0%) missing values Missing
genre_4 has 280 (7.9%) missing values Missing
genre_5 has 310 (8.7%) missing values Missing
genre_6 has 338 (9.5%) missing values Missing
genre_7 has 378 (10.6%) missing values Missing
genre_8 has 414 (11.7%) missing values Missing
genre_9 has 450 (12.7%) missing values Missing
genre_0_weight has 150 (4.2%) missing values Missing
genre_1_weight has 197 (5.5%) missing values Missing
genre_2_weight has 228 (6.4%) missing values Missing
genre_3_weight has 248 (7.0%) missing values Missing
genre_4_weight has 280 (7.9%) missing values Missing
genre_5_weight has 310 (8.7%) missing values Missing
genre_6_weight has 338 (9.5%) missing values Missing
genre_7_weight has 378 (10.6%) missing values Missing
genre_8_weight has 414 (11.7%) missing values Missing
genre_9_weight has 450 (12.7%) missing values Missing
price has 3007 (84.7%) missing values Missing
description is uniformly distributed Uniform
lexile_measure is uniformly distributed Uniform
rating_value_0 has 48 (1.4%) zeros Zeros
rating_count_0 has 48 (1.4%) zeros Zeros
genre_8_weight has 51 (1.4%) zeros Zeros
genre_9_weight has 69 (1.9%) zeros Zeros

Reproduction

Analysis started2021-05-07 08:17:27.664006
Analysis finished2021-05-07 08:18:16.449384
Duration48.79 seconds
Software versionpandas-profiling v2.11.0
Download configurationconfig.yaml

Variables

author_id
Categorical

HIGH CARDINALITY

Distinct2412
Distinct (%)68.1%
Missing10
Missing (%)0.3%
Memory size27.9 KiB
author2538
 
14
author2672
 
13
author0344
 
12
author2373
 
12
author1076
 
11
Other values (2407)
3478 

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters35400
Distinct characters16
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1818 ?
Unique (%)51.4%

Sample

1st rowauthor2106
2nd rowauthor1018
3rd rowauthor1087
4th rowauthor1295
5th rowauthor2622
ValueCountFrequency (%)
author253814
 
0.4%
author267213
 
0.4%
author034412
 
0.3%
author237312
 
0.3%
author107611
 
0.3%
author044111
 
0.3%
author056710
 
0.3%
author11149
 
0.3%
author05019
 
0.3%
author18709
 
0.3%
Other values (2402)3430
96.6%
(Missing)10
 
0.3%
2021-05-07T15:18:16.650568image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
author253814
 
0.4%
author267213
 
0.4%
author237312
 
0.3%
author034412
 
0.3%
author044111
 
0.3%
author107611
 
0.3%
author056710
 
0.3%
author12389
 
0.3%
author18709
 
0.3%
author03699
 
0.3%
Other values (2402)3430
96.9%

Most occurring characters

ValueCountFrequency (%)
a3540
10.0%
u3540
10.0%
t3540
10.0%
h3540
10.0%
o3540
10.0%
r3540
10.0%
12377
 
6.7%
22221
 
6.3%
02188
 
6.2%
31124
 
3.2%
Other values (6)6250
17.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter21240
60.0%
Decimal Number14160
40.0%

Most frequent character per category

ValueCountFrequency (%)
12377
16.8%
22221
15.7%
02188
15.5%
31124
7.9%
41103
7.8%
71083
7.6%
81062
7.5%
51037
7.3%
6999
7.1%
9966
6.8%
ValueCountFrequency (%)
a3540
16.7%
u3540
16.7%
t3540
16.7%
h3540
16.7%
o3540
16.7%
r3540
16.7%

Most occurring scripts

ValueCountFrequency (%)
Latin21240
60.0%
Common14160
40.0%

Most frequent character per script

ValueCountFrequency (%)
12377
16.8%
22221
15.7%
02188
15.5%
31124
7.9%
41103
7.8%
71083
7.6%
81062
7.5%
51037
7.3%
6999
7.1%
9966
6.8%
ValueCountFrequency (%)
a3540
16.7%
u3540
16.7%
t3540
16.7%
h3540
16.7%
o3540
16.7%
r3540
16.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII35400
100.0%

Most frequent character per block

ValueCountFrequency (%)
a3540
10.0%
u3540
10.0%
t3540
10.0%
h3540
10.0%
o3540
10.0%
r3540
10.0%
12377
 
6.7%
22221
 
6.3%
02188
 
6.2%
31124
 
3.2%
Other values (6)6250
17.7%

description
Categorical

HIGH CARDINALITY
MISSING
UNIFORM

Distinct3400
Distinct (%)96.8%
Missing38
Missing (%)1.1%
Memory size27.9 KiB
On a warm summer morning in North Carthage, Missouri, it is Nick and Amy Dunne's fifth wedding anniversary. Presents are being wrapped and reservations are being made when Nick's clever and beautiful wife disappears from their rented McMansion on the Mississippi River. Husband-of-the-Year Nick isn't doing himself any favors with cringe-worthy daydreams about the slope and shape of his wife's head, but passages from Amy's diary reveal the alpha-girl perfectionist could have put anyone dangerously on edge. Under mounting pressure from the police and the media--as well as Amy's fiercely doting parents--the town golden boy parades an endless series of lies, deceits, and inappropriate behavior. Nick is oddly evasive, and he's definitely bitter--but is he really a killer? As the cops close in, every couple in town is soon wondering how well they know the one that they love. With his twin sister, Margo, at his side, Nick stands by his innocence. Trouble is, if Nick didn't do it, where is that beautiful wife? And what was in that silvery gift box hidden in the back of her bedroom closet?
 
3
then.When Sam met Grace, he was a wolf and she was a girl. Eventually he found a way to become a boy, and their love moved from curious distance to the intense closeness of shared lives.now.That should have been the end of their story. But Grace was not meant to stay human. Now she is the wolf. And the wolves of Mercy Falls are about to be killed in one final, spectacular hunt.forever.Sam would do anything for Grace. But can one boy and one love really change a hostile, predatory world? The past, the present, and the future are about to collide in one pure moment--a moment of death or life, farewell or forever.
 
3
An alternate cover edition of ISBN 9780062498533 can be found here.Sixteen-year-old Starr Carter moves between two worlds: the poor neighborhood where she lives and the fancy suburban prep school she attends. The uneasy balance between these worlds is shattered when Starr witnesses the fatal shooting of her childhood best friend Khalil at the hands of a police officer. Khalil was unarmed.Soon afterward, his death is a national headline. Some are calling him a thug, maybe even a drug dealer and a gangbanger. Protesters are taking to the streets in Khalil’s name. Some cops and the local drug lord try to intimidate Starr and her family. What everyone wants to know is: what really went down that night? And the only person alive who can answer that is Starr.But what Starr does—or does not—say could upend her community. It could also endanger her life.Inspired by the Black Lives Matter movement, this is a powerful and gripping YA novel about one girl's struggle for justice.
 
3
"I live for the dream that my children will be born free," she says. "That they will be what they like. That they will own the land their father gave them.""I live for you," I say sadly.Eo kisses my cheek. "Then you must live for more."Darrow is a Red, a member of the lowest caste in the color-coded society of the future. Like his fellow Reds, he works all day, believing that he and his people are making the surface of Mars livable for future generations.Yet he spends his life willingly, knowing that his blood and sweat will one day result in a better world for his children.But Darrow and his kind have been betrayed. Soon he discovers that humanity already reached the surface generations ago. Vast cities and sprawling parks spread across the planet. Darrow—and Reds like him—are nothing more than slaves to a decadent ruling class.Inspired by a longing for justice, and driven by the memory of lost love, Darrow sacrifices everything to infiltrate the legendary Institute, a proving ground for the dominant Gold caste, where the next generation of humanity's overlords struggle for power. He will be forced to compete for his life and the very future of civilization against the best and most brutal of Society's ruling class. There, he will stop at nothing to bring down his enemies... even if it means he has to become one of them to do so.
 
3
No one wants what no one wants.And how do we even know what we want? How do we know we’re ready to take it?Edie is stumbling her way through her twenties—sharing a subpar apartment in Bushwick, clocking in and out of her admin job, making a series of inappropriate sexual choices. She is also haltingly, fitfully giving heat and air to the art that simmers inside her. And then she meets Eric, a digital archivist with a family in New Jersey, including an autopsist wife who has agreed to an open marriage—with rules.As if navigating the constantly shifting landscapes of contemporary sexual manners and racial politics weren’t hard enough, Edie finds herself unemployed and invited into Eric’s home—though not by Eric. She becomes a hesitant ally to his wife and a de facto role model to his adopted daughter. Edie may be the only Black woman young Akila knows.Irresistibly unruly and strikingly beautiful, razor-sharp and slyly comic, sexually charged and utterly absorbing, Raven Leilani’s Luster is a portrait of a young woman trying to make sense of her life—her hunger, her anger—in a tumultuous era. It is also a haunting, aching description of how hard it is to believe in your own talent, and the unexpected influences that bring us into ourselves along the way.
 
3
Other values (3395)
3497 

Length

Max length5216
Median length1047
Mean length1106.292995
Min length22

Characters and Unicode

Total characters3885301
Distinct characters222
Distinct categories18 ?
Distinct scripts2 ?
Distinct blocks15 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3293 ?
Unique (%)93.8%

Sample

1st rowJust after the Second World War, in the small English village of Chawton, an unusual but like-minded group of people band together to attempt something remarkable.One hundred and fifty years ago, Chawton was the final home of Jane Austen, one of England's finest novelists. Now it's home to a few distant relatives and their diminishing estate. With the last bit of Austen's legacy threatened, a group of disparate individuals come together to preserve both Jane Austen's home and her legacy. These people—a laborer, a young widow, the local doctor, and a movie star, among others—could not be more different and yet they are united in their love for the works and words of Austen. As each of them endures their own quiet struggle with loss and trauma, some from the recent war, others from more distant tragedies, they rally together to create the Jane Austen Society.
2nd rowBlame it on Hawaii’s rainbows, sparkling beaches, fruity cocktails, and sensuous breezes. For Heather Diamond, there for a summer course on China, a sea change began when romance bloomed with Fred, an ethnomusicologist from Hong Kong.One night under a full moon, Fred tells Heather the story of Chang’e, the moon goddess. He points out how the shadows form a rabbit pounding an elixir of immortality, but all Heather sees in the moon is a man’s face.Returning to her teaching job in Texas, Heather wonders if the whirlwind affair was a moment of madness. She is, after all, forty-five years old, married, a mother and grandmother.Rabbit in the Moon follows Heather and Fred’s relationship as well as Heather’s challenges with multiple mid-life reinventions, such as moving to Hawaii, entering a Ph.D. program, and living in a dorm with students half her age.When Fred goes on sabbatical, Heather finds herself on the Hong Kong island of Cheung Chau with his large, boisterous family. For an independent, reserved American, adjusting to his extended family isn’t easy. She wants to fit in, but is culture shocked by the lack of privacy, the language barrier, and the Chinese aesthetic of renao (“hot & noisy”).Life on Cheung Chau is overwhelming but also wondrous. Heather chronicles family celebrations, ancestor rituals, and a rich cycle of festivals like the Hungry Ghosts Festival, Chinese New Year, and the Bun Festival. Her descriptions of daily life and traditions are exquisite, seamlessly combining the insights of an ethnographer with the fascination of a curious newcomer who gradually transitions topart of the family.Ultimately, Heather’s experiences abroad make her realize what she has overlooked with her own family back in the United States, and she sets about making amends.Moving between Hawaii, Hong Kong, and the continental US, Rabbit in the Moon is an honest, finely crafted meditation on intercultural marriage, the importance of family, and finding the courage to follow your dreams.
3rd rowThe Pulitzer Prize–winning, bestselling author of The Warmth of Other Suns examines the unspoken caste system that has shaped America and shows how our lives today are still defined by a hierarchy of human divisions.“As we go about our daily lives, caste is the wordless usher in a darkened theater, flashlight cast down in the aisles, guiding us to our assigned seats for a performance. The hierarchy of caste is not about feelings or morality. It is about power—which groups have it and which do not.”In this brilliant book, Isabel Wilkerson gives us a masterful portrait of an unseen phenomenon in America as she explores, through an immersive, deeply researched narrative and stories about real people, how America today and throughout its history has been shaped by a hidden caste system, a rigid hierarchy of human rankings.Beyond race, class, or other factors, there is a powerful caste system that influences people’s lives and behavior and the nation’s fate. Linking the caste systems of America, India, and Nazi Germany, Wilkerson explores eight pillars that underlie caste systems across civilizations, including divine will, bloodlines, stigma, and more. Using riveting stories about people—including Martin Luther King, Jr., baseball’s Satchel Paige, a single father and his toddler son, Wilkerson herself, and many others—she shows the ways that the insidious undertow of caste is experienced every day. She documents how the Nazis studied the racial systems in America to plan their out-cast of the Jews; she discusses why the cruel logic of caste requires that there be a bottom rung for those in the middle to measure themselves against; she writes about the surprising health costs of caste, in depression and life expectancy, and the effects of this hierarchy on our culture and politics. Finally, she points forward to ways America can move beyond the artificial and destructive separations of human divisions, toward hope in our common humanity.
4th rowTHINGS ARE ABOUT TO GET SERIOUS FOR HARRY DRESDEN, CHICAGO’S ONLY PROFESSIONAL WIZARD, in the next entry in the #1 New York Times bestselling Dresden Files.Harry has faced terrible odds before. He has a long history of fighting enemies above his weight class. The Red Court of vampires. The fallen angels of the Order of the Blackened Denarius. The Outsiders.But this time it’s different. A being more powerful and dangerous on an order of magnitude beyond what the world has seen in a millennium is coming. And she’s bringing an army. The Last Titan has declared war on the city of Chicago, and has come to subjugate humanity, obliterating any who stand in her way.Harry’s mission is simple but impossible: Save the city by killing a Titan. And the attempt will change Harry’s life, Chicago, and the mortal world forever.---Includes the short story "Christmas Eve".
5th rowThe Romanovs were the most successful dynasty of modern times, ruling a sixth of the world’s surface for three centuries. How did one family turn a war-ruined principality into the world’s greatest empire? And how did they lose it all? This is the intimate story of twenty tsars and tsarinas, some touched by genius, some by madness, but all inspired by holy autocracy and imperial ambition. Simon Sebag Montefiore’s gripping chronicle reveals their secret world of unlimited power and ruthless empire-building, overshadowed by palace conspiracy, family rivalries, sexual decadence and wild extravagance, with a global cast of adventurers, courtesans, revolutionaries and poets, from Ivan the Terrible to Tolstoy and Pushkin, to Bismarck, Lincoln, Queen Victoria and Lenin.
ValueCountFrequency (%)
On a warm summer morning in North Carthage, Missouri, it is Nick and Amy Dunne's fifth wedding anniversary. Presents are being wrapped and reservations are being made when Nick's clever and beautiful wife disappears from their rented McMansion on the Mississippi River. Husband-of-the-Year Nick isn't doing himself any favors with cringe-worthy daydreams about the slope and shape of his wife's head, but passages from Amy's diary reveal the alpha-girl perfectionist could have put anyone dangerously on edge. Under mounting pressure from the police and the media--as well as Amy's fiercely doting parents--the town golden boy parades an endless series of lies, deceits, and inappropriate behavior. Nick is oddly evasive, and he's definitely bitter--but is he really a killer? As the cops close in, every couple in town is soon wondering how well they know the one that they love. With his twin sister, Margo, at his side, Nick stands by his innocence. Trouble is, if Nick didn't do it, where is that beautiful wife? And what was in that silvery gift box hidden in the back of her bedroom closet?3
 
0.1%
then.When Sam met Grace, he was a wolf and she was a girl. Eventually he found a way to become a boy, and their love moved from curious distance to the intense closeness of shared lives.now.That should have been the end of their story. But Grace was not meant to stay human. Now she is the wolf. And the wolves of Mercy Falls are about to be killed in one final, spectacular hunt.forever.Sam would do anything for Grace. But can one boy and one love really change a hostile, predatory world? The past, the present, and the future are about to collide in one pure moment--a moment of death or life, farewell or forever.3
 
0.1%
An alternate cover edition of ISBN 9780062498533 can be found here.Sixteen-year-old Starr Carter moves between two worlds: the poor neighborhood where she lives and the fancy suburban prep school she attends. The uneasy balance between these worlds is shattered when Starr witnesses the fatal shooting of her childhood best friend Khalil at the hands of a police officer. Khalil was unarmed.Soon afterward, his death is a national headline. Some are calling him a thug, maybe even a drug dealer and a gangbanger. Protesters are taking to the streets in Khalil’s name. Some cops and the local drug lord try to intimidate Starr and her family. What everyone wants to know is: what really went down that night? And the only person alive who can answer that is Starr.But what Starr does—or does not—say could upend her community. It could also endanger her life.Inspired by the Black Lives Matter movement, this is a powerful and gripping YA novel about one girl's struggle for justice.3
 
0.1%
"I live for the dream that my children will be born free," she says. "That they will be what they like. That they will own the land their father gave them.""I live for you," I say sadly.Eo kisses my cheek. "Then you must live for more."Darrow is a Red, a member of the lowest caste in the color-coded society of the future. Like his fellow Reds, he works all day, believing that he and his people are making the surface of Mars livable for future generations.Yet he spends his life willingly, knowing that his blood and sweat will one day result in a better world for his children.But Darrow and his kind have been betrayed. Soon he discovers that humanity already reached the surface generations ago. Vast cities and sprawling parks spread across the planet. Darrow—and Reds like him—are nothing more than slaves to a decadent ruling class.Inspired by a longing for justice, and driven by the memory of lost love, Darrow sacrifices everything to infiltrate the legendary Institute, a proving ground for the dominant Gold caste, where the next generation of humanity's overlords struggle for power. He will be forced to compete for his life and the very future of civilization against the best and most brutal of Society's ruling class. There, he will stop at nothing to bring down his enemies... even if it means he has to become one of them to do so.3
 
0.1%
No one wants what no one wants.And how do we even know what we want? How do we know we’re ready to take it?Edie is stumbling her way through her twenties—sharing a subpar apartment in Bushwick, clocking in and out of her admin job, making a series of inappropriate sexual choices. She is also haltingly, fitfully giving heat and air to the art that simmers inside her. And then she meets Eric, a digital archivist with a family in New Jersey, including an autopsist wife who has agreed to an open marriage—with rules.As if navigating the constantly shifting landscapes of contemporary sexual manners and racial politics weren’t hard enough, Edie finds herself unemployed and invited into Eric’s home—though not by Eric. She becomes a hesitant ally to his wife and a de facto role model to his adopted daughter. Edie may be the only Black woman young Akila knows.Irresistibly unruly and strikingly beautiful, razor-sharp and slyly comic, sexually charged and utterly absorbing, Raven Leilani’s Luster is a portrait of a young woman trying to make sense of her life—her hunger, her anger—in a tumultuous era. It is also a haunting, aching description of how hard it is to believe in your own talent, and the unexpected influences that bring us into ourselves along the way.3
 
0.1%
A striking and surprising debut novel from an exhilarating new voice, Such a Fun Age is a page-turning and big-hearted story about race and privilege, set around a young black babysitter, her well-intentioned employer, and a surprising connection that threatens to undo them both.Alix Chamberlain is a woman who gets what she wants and has made a living, with her confidence-driven brand, showing other women how to do the same. So she is shocked when her babysitter, Emira Tucker, is confronted while watching the Chamberlains' toddler one night, walking the aisles of their local high-end supermarket. The store's security guard, seeing a young black woman out late with a white child, accuses Emira of kidnapping two-year-old Briar. A small crowd gathers, a bystander films everything, and Emira is furious and humiliated. Alix resolves to make things right.But Emira herself is aimless, broke, and wary of Alix's desire to help. At twenty-five, she is about to lose her health insurance and has no idea what to do with her life. When the video of Emira unearths someone from Alix's past, both women find themselves on a crash course that will upend everything they think they know about themselves, and each other.With empathy and piercing social commentary, Such a Fun Age explores the stickiness of transactional relationships, what it means to make someone family, and the complicated reality of being a grown up. It is a searing debut for our times.2
 
0.1%
In a sprawling mansion filled with peculiar treasures, January Scaller is a curiosity herself. As the ward of the wealthy Mr. Locke, she feels little different from the artifacts that decorate the halls: carefully maintained, largely ignored, and utterly out of place.Then she finds a strange book. A book that carries the scent of other worlds, and tells a tale of secret doors, of love, adventure and danger. Each page turn reveals impossible truths about the world and January discovers a story increasingly entwined with her own.2
 
0.1%
A fallen boxer.A woman with a broken dream.A competition...He even makes me forget my name. One night was all it took, and I forgot everything and anything except the sexy fighter in the ring who sets my mind ablaze and my body on fire with wanting...Remington Tate is the strongest, most confusing man I've ever met in my life.He’s the star of the dangerous underground fighting circuit, and I’m drawn to him as I've never been drawn to anything in my life. I forget who I am, what I want, with just one look from him. When he’s near, I need to remind myself that I am strong—but he is stronger. And now it’s my job to keep his body working like a perfect machine, his taut muscles primed and ready to break the bones of his next opponents...But the one he’s most threatening to, now, is me.I want him. I want him without fear. Without reservations.If only I knew for sure what it is that he wants from me?2
 
0.1%
In the grip of the worst drought in a century, the farming community of Kiewarra is facing life and death choices daily when three members of a local family are found brutally slain.Federal Police investigator Aaron Falk reluctantly returns to his hometown for the funeral of his childhood friend, loath to face the townsfolk who turned their backs on him twenty years earlier. But as questions mount, Falk is forced to probe deeper into the deaths of the Hadler family. Because Falk and Luke Hadler shared a secret. A secret Falk thought was long buried. A secret Luke's death now threatens to bring to the surface in this small Australian town, as old wounds in bleed into new ones.A small town hides big secrets in this atmospheric, page-turning debut mystery by award-winning author Jane Harper.2
 
0.1%
BEING CONNECTED TO DAEMON BLACK SUCKS… Thanks to his alien mojo, Daemon’s determined to prove what he feels for me is more than a product of our bizarro connection. So I’ve sworn him off, even though he’s running more hot than cold these days. But we’ve got bigger problems.SOMETHING WORSE THAN ARUM HAS COME TO TOWN The Department of Defense is here. If they ever find out what Daemon can do and that we’re linked, I’m a goner. So is he. And there’s this new boy in school who’s got a secret of his own. He knows what’s happened to me and he can help, but to do so, I have to lie to Daemon and stay away from him. Like that’s possible. Against all common sense, I’m falling for Daemon. Hard.BUT THEN, EVERYTHING CHANGES I’ve seen someone who shouldn’t be alive. And I have to tell Daemon, even though I know he’s never going to stop searching until he gets the truth. What happened to his brother? Who betrayed him? And what does the DOD want from them—from me?NO ONE IS WHO THEY SEEM. AND NOT EVERYONE WILL SURVIVE THE LIES.2
 
0.1%
Other values (3390)3487
98.2%
(Missing)38
 
1.1%
2021-05-07T15:18:16.909802image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
the35147
 
5.5%
and24188
 
3.8%
of19823
 
3.1%
a18388
 
2.9%
to17410
 
2.7%
in10750
 
1.7%
her8190
 
1.3%
is8087
 
1.3%
that5679
 
0.9%
with5506
 
0.9%
Other values (50641)490121
76.2%

Most occurring characters

ValueCountFrequency (%)
639508
16.5%
e379110
 
9.8%
t257341
 
6.6%
a246305
 
6.3%
o228344
 
5.9%
n225747
 
5.8%
i219338
 
5.6%
s205258
 
5.3%
r200702
 
5.2%
h161369
 
4.2%
Other values (212)1122279
28.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter3017773
77.7%
Space Separator641190
 
16.5%
Uppercase Letter103842
 
2.7%
Other Punctuation89946
 
2.3%
Dash Punctuation14859
 
0.4%
Final Punctuation7500
 
0.2%
Decimal Number7138
 
0.2%
Initial Punctuation908
 
< 0.1%
Close Punctuation825
 
< 0.1%
Open Punctuation799
 
< 0.1%
Other values (8)521
 
< 0.1%

Most frequent character per category

ValueCountFrequency (%)
e379110
12.6%
t257341
 
8.5%
a246305
 
8.2%
o228344
 
7.6%
n225747
 
7.5%
i219338
 
7.3%
s205258
 
6.8%
r200702
 
6.7%
h161369
 
5.3%
l133482
 
4.4%
Other values (82)760777
25.2%
ValueCountFrequency (%)
A10288
 
9.9%
T9693
 
9.3%
S8163
 
7.9%
B7000
 
6.7%
I6219
 
6.0%
C5641
 
5.4%
W5507
 
5.3%
M5369
 
5.2%
H4647
 
4.5%
N4240
 
4.1%
Other values (41)37075
35.7%
ValueCountFrequency (%)
,43652
48.5%
.31796
35.4%
'6023
 
6.7%
:1919
 
2.1%
"1848
 
2.1%
?1807
 
2.0%
;976
 
1.1%
!676
 
0.8%
386
 
0.4%
#247
 
0.3%
Other values (7)616
 
0.7%
ValueCountFrequency (%)
11759
24.6%
01607
22.5%
2873
12.2%
9786
11.0%
8408
 
5.7%
5408
 
5.7%
3371
 
5.2%
7318
 
4.5%
4313
 
4.4%
6295
 
4.1%
ValueCountFrequency (%)
14
41.2%
5
 
14.7%
4
 
11.8%
3
 
8.8%
®3
 
8.8%
©2
 
5.9%
1
 
2.9%
🏆1
 
2.9%
1
 
2.9%
ValueCountFrequency (%)
-11171
75.2%
3326
 
22.4%
301
 
2.0%
55
 
0.4%
5
 
< 0.1%
1
 
< 0.1%
ValueCountFrequency (%)
+19
45.2%
|8
19.0%
>7
 
16.7%
~4
 
9.5%
¬2
 
4.8%
<2
 
4.8%
ValueCountFrequency (%)
639508
99.7%
 1675
 
0.3%
4
 
< 0.1%
 3
 
< 0.1%
ValueCountFrequency (%)
319
97.9%
—3
 
0.9%
3
 
0.9%
…1
 
0.3%
ValueCountFrequency (%)
­61
91.0%
3
 
4.5%
2
 
3.0%
1
 
1.5%
ValueCountFrequency (%)
6615
88.2%
883
 
11.8%
»2
 
< 0.1%
ValueCountFrequency (%)
(777
97.2%
[20
 
2.5%
{2
 
0.3%
ValueCountFrequency (%)
870
95.8%
36
 
4.0%
«2
 
0.2%
ValueCountFrequency (%)
)803
97.3%
]20
 
2.4%
}2
 
0.2%
ValueCountFrequency (%)
$40
97.6%
1
 
2.4%
ValueCountFrequency (%)
½3
75.0%
¼1
 
25.0%
ValueCountFrequency (%)
ʼ3
75.0%
ˈ1
 
25.0%
ValueCountFrequency (%)
3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin3121363
80.3%
Common763938
 
19.7%

Most frequent character per script

ValueCountFrequency (%)
639508
83.7%
,43652
 
5.7%
.31796
 
4.2%
-11171
 
1.5%
6615
 
0.9%
'6023
 
0.8%
3326
 
0.4%
:1919
 
0.3%
"1848
 
0.2%
?1807
 
0.2%
Other values (120)16273
 
2.1%
ValueCountFrequency (%)
e379110
12.1%
t257341
 
8.2%
a246305
 
7.9%
o228344
 
7.3%
n225747
 
7.2%
i219338
 
7.0%
s205258
 
6.6%
r200702
 
6.4%
h161369
 
5.2%
l133482
 
4.3%
Other values (82)864367
27.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII3870146
99.6%
Punctuation12610
 
0.3%
None2253
 
0.1%
Math Alphanum252
 
< 0.1%
Misc Symbols14
 
< 0.1%
Specials5
 
< 0.1%
Modifier Letters4
 
< 0.1%
Box Drawing4
 
< 0.1%
Latin Ext Additional4
 
< 0.1%
Geometric Shapes3
 
< 0.1%
Other values (5)6
 
< 0.1%

Most frequent character per block

ValueCountFrequency (%)
639508
16.5%
e379110
 
9.8%
t257341
 
6.6%
a246305
 
6.4%
o228344
 
5.9%
n225747
 
5.8%
i219338
 
5.7%
s205258
 
5.3%
r200702
 
5.2%
h161369
 
4.2%
Other values (82)1107124
28.6%
ValueCountFrequency (%)
6615
52.5%
3326
26.4%
883
 
7.0%
870
 
6.9%
386
 
3.1%
301
 
2.4%
121
 
1.0%
55
 
0.4%
36
 
0.3%
5
 
< 0.1%
Other values (5)12
 
0.1%
ValueCountFrequency (%)
 1675
74.3%
é154
 
6.8%
­61
 
2.7%
á48
 
2.1%
·38
 
1.7%
í30
 
1.3%
ï19
 
0.8%
ä18
 
0.8%
ó17
 
0.8%
ö15
 
0.7%
Other values (42)178
 
7.9%
ValueCountFrequency (%)
𝒊19
 
7.5%
𝒆19
 
7.5%
𝒕17
 
6.7%
𝒐17
 
6.7%
𝒓17
 
6.7%
𝒂16
 
6.3%
𝒔13
 
5.2%
𝑻12
 
4.8%
𝒉12
 
4.8%
𝒏9
 
3.6%
Other values (41)101
40.1%
ValueCountFrequency (%)
3
100.0%
ValueCountFrequency (%)
1
100.0%
ValueCountFrequency (%)
5
100.0%
ValueCountFrequency (%)
ʼ3
75.0%
ˈ1
 
25.0%
ValueCountFrequency (%)
14
100.0%
ValueCountFrequency (%)
ə1
100.0%
ValueCountFrequency (%)
2
100.0%
ValueCountFrequency (%)
4
100.0%
ValueCountFrequency (%)
ế4
100.0%
ValueCountFrequency (%)
1
100.0%
ValueCountFrequency (%)
1
100.0%

bookformat
Categorical

Distinct13
Distinct (%)0.4%
Missing13
Missing (%)0.4%
Memory size27.9 KiB
Hardcover
2524 
Paperback
714 
Kindle Edition
 
201
Mass Market Paperback
 
43
ebook
 
42
Other values (8)
 
13

Length

Max length21
Median length9
Mean length9.384223919
Min length4

Characters and Unicode

Total characters33192
Distinct characters34
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique5 ?
Unique (%)0.1%

Sample

1st rowHardcover
2nd rowPaperback
3rd rowHardcover
4th rowHardcover
5th rowHardcover
ValueCountFrequency (%)
Hardcover2524
71.1%
Paperback714
 
20.1%
Kindle Edition201
 
5.7%
Mass Market Paperback43
 
1.2%
ebook42
 
1.2%
Nook3
 
0.1%
Board Book3
 
0.1%
paperback2
 
0.1%
Spiral-bound1
 
< 0.1%
Trade Paperback1
 
< 0.1%
Other values (3)3
 
0.1%
(Missing)13
 
0.4%
2021-05-07T15:18:17.119994image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
hardcover2524
65.9%
paperback760
 
19.8%
edition201
 
5.2%
kindle201
 
5.2%
mass43
 
1.1%
market43
 
1.1%
ebook42
 
1.1%
board3
 
0.1%
book3
 
0.1%
nook3
 
0.1%
Other values (6)7
 
0.2%

Most occurring characters

ValueCountFrequency (%)
r5858
17.6%
a4136
12.5%
e3571
10.8%
c3285
9.9%
d2933
8.8%
o2827
8.5%
H2524
7.6%
v2524
7.6%
k852
 
2.6%
b804
 
2.4%
Other values (24)3878
11.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter29112
87.7%
Uppercase Letter3786
 
11.4%
Space Separator293
 
0.9%
Dash Punctuation1
 
< 0.1%

Most frequent character per category

ValueCountFrequency (%)
r5858
20.1%
a4136
14.2%
e3571
12.3%
c3285
11.3%
d2933
10.1%
o2827
9.7%
v2524
8.7%
k852
 
2.9%
b804
 
2.8%
p763
 
2.6%
Other values (10)1559
 
5.4%
ValueCountFrequency (%)
H2524
66.7%
P758
 
20.0%
K201
 
5.3%
E201
 
5.3%
M86
 
2.3%
B8
 
0.2%
N3
 
0.1%
U1
 
< 0.1%
L1
 
< 0.1%
S1
 
< 0.1%
Other values (2)2
 
0.1%
ValueCountFrequency (%)
293
100.0%
ValueCountFrequency (%)
-1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin32898
99.1%
Common294
 
0.9%

Most frequent character per script

ValueCountFrequency (%)
r5858
17.8%
a4136
12.6%
e3571
10.9%
c3285
10.0%
d2933
8.9%
o2827
8.6%
H2524
7.7%
v2524
7.7%
k852
 
2.6%
b804
 
2.4%
Other values (22)3584
10.9%
ValueCountFrequency (%)
293
99.7%
-1
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII33192
100.0%

Most frequent character per block

ValueCountFrequency (%)
r5858
17.6%
a4136
12.5%
e3571
10.8%
c3285
9.9%
d2933
8.8%
o2827
8.5%
H2524
7.6%
v2524
7.6%
k852
 
2.6%
b804
 
2.4%
Other values (24)3878
11.7%

bookedition
Categorical

HIGH CARDINALITY
MISSING

Distinct68
Distinct (%)29.4%
Missing3319
Missing (%)93.5%
Memory size27.9 KiB
First Edition
76 
1st Edition
20 
Trade
13 
First Edition (U.S.)
11 
1st
 
9
Other values (63)
102 

Length

Max length55
Median length13
Mean length12.45454545
Min length1

Characters and Unicode

Total characters2877
Distinct characters56
Distinct categories8 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique46 ?
Unique (%)19.9%

Sample

1st rowFirst Edition
2nd rowFirst Scribner Hardcover Edition
3rd rowFirst
4th rowEnglish edition
5th rowFirst US Edition
ValueCountFrequency (%)
First Edition76
 
2.1%
1st Edition20
 
0.6%
Trade13
 
0.4%
First Edition (U.S.)11
 
0.3%
1st9
 
0.3%
1st edition7
 
0.2%
First7
 
0.2%
US5
 
0.1%
Large Print5
 
0.1%
1st US edition4
 
0.1%
Other values (58)74
 
2.1%
(Missing)3319
93.5%
2021-05-07T15:18:17.357210image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
edition158
33.4%
first118
24.9%
1st44
 
9.3%
us16
 
3.4%
trade13
 
2.7%
u.s12
 
2.5%
print7
 
1.5%
large6
 
1.3%
deckle5
 
1.1%
15
 
1.1%
Other values (61)89
18.8%

Most occurring characters

ValueCountFrequency (%)
i479
16.6%
t347
12.1%
246
8.6%
n205
 
7.1%
d195
 
6.8%
o191
 
6.6%
r185
 
6.4%
s181
 
6.3%
E140
 
4.9%
F119
 
4.1%
Other values (46)589
20.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter2077
72.2%
Uppercase Letter423
 
14.7%
Space Separator246
 
8.6%
Decimal Number63
 
2.2%
Other Punctuation36
 
1.3%
Open Punctuation15
 
0.5%
Close Punctuation15
 
0.5%
Dash Punctuation2
 
0.1%

Most frequent character per category

ValueCountFrequency (%)
i479
23.1%
t347
16.7%
n205
9.9%
d195
9.4%
o191
 
9.2%
r185
 
8.9%
s181
 
8.7%
e108
 
5.2%
a53
 
2.6%
c26
 
1.3%
Other values (12)107
 
5.2%
ValueCountFrequency (%)
E140
33.1%
F119
28.1%
S45
 
10.6%
U33
 
7.8%
T14
 
3.3%
P13
 
3.1%
A8
 
1.9%
C8
 
1.9%
L7
 
1.7%
B6
 
1.4%
Other values (11)30
 
7.1%
ValueCountFrequency (%)
.24
66.7%
,6
 
16.7%
&2
 
5.6%
/2
 
5.6%
'2
 
5.6%
ValueCountFrequency (%)
150
79.4%
29
 
14.3%
03
 
4.8%
81
 
1.6%
ValueCountFrequency (%)
246
100.0%
ValueCountFrequency (%)
(15
100.0%
ValueCountFrequency (%)
)15
100.0%
ValueCountFrequency (%)
-2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin2500
86.9%
Common377
 
13.1%

Most frequent character per script

ValueCountFrequency (%)
i479
19.2%
t347
13.9%
n205
8.2%
d195
7.8%
o191
 
7.6%
r185
 
7.4%
s181
 
7.2%
E140
 
5.6%
F119
 
4.8%
e108
 
4.3%
Other values (33)350
14.0%
ValueCountFrequency (%)
246
65.3%
150
 
13.3%
.24
 
6.4%
(15
 
4.0%
)15
 
4.0%
29
 
2.4%
,6
 
1.6%
03
 
0.8%
&2
 
0.5%
-2
 
0.5%
Other values (3)5
 
1.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII2877
100.0%

Most frequent character per block

ValueCountFrequency (%)
i479
16.6%
t347
12.1%
246
8.6%
n205
 
7.1%
d195
 
6.8%
o191
 
6.6%
r185
 
6.4%
s181
 
6.3%
E140
 
4.9%
F119
 
4.1%
Other values (46)589
20.5%

pages
Real number (ℝ≥0)

MISSING

Distinct574
Distinct (%)16.6%
Missing99
Missing (%)2.8%
Infinite0
Infinite (%)0.0%
Mean331.8942336
Minimum20
Maximum1248
Zeros0
Zeros (%)0.0%
Memory size27.9 KiB
2021-05-07T15:18:17.463306image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum20
5-th percentile64
Q1256
median330
Q3400
95-th percentile576
Maximum1248
Range1228
Interquartile range (IQR)144

Descriptive statistics

Standard deviation147.4723579
Coefficient of variation (CV)0.4443354027
Kurtosis3.328039563
Mean331.8942336
Median Absolute Deviation (MAD)71
Skewness0.7880064332
Sum1145367
Variance21748.09635
MonotocityNot monotonic
2021-05-07T15:18:17.576409image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
32098
 
2.8%
35296
 
2.7%
33688
 
2.5%
28876
 
2.1%
25676
 
2.1%
38467
 
1.9%
30467
 
1.9%
24060
 
1.7%
27258
 
1.6%
4058
 
1.6%
Other values (564)2707
76.3%
(Missing)99
 
2.8%
ValueCountFrequency (%)
201
 
< 0.1%
242
 
0.1%
281
 
< 0.1%
311
 
< 0.1%
3252
1.5%
ValueCountFrequency (%)
12481
< 0.1%
12321
< 0.1%
11501
< 0.1%
11252
0.1%
10981
< 0.1%

published_date
Categorical

HIGH CARDINALITY
MISSING

Distinct278
Distinct (%)48.9%
Missing2982
Missing (%)84.0%
Memory size27.9 KiB
April 3, 2012
 
9
May 18, 2021
 
9
May 3, 2011
 
8
March 1, 2011
 
8
July 10, 2012
 
7
Other values (273)
527 

Length

Max length25
Median length14
Mean length14.25352113
Min length11

Characters and Unicode

Total characters8096
Distinct characters48
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique150 ?
Unique (%)26.4%

Sample

1st rowFebruary 28, 2012
2nd rowJanuary 31, 2013
3rd rowMarch 25, 2014
4th rowAugust 16, 2011
5th rowMay 5, 2019
ValueCountFrequency (%)
April 3, 20129
 
0.3%
May 18, 20219
 
0.3%
May 3, 20118
 
0.2%
March 1, 20118
 
0.2%
July 10, 20127
 
0.2%
January 1, 20127
 
0.2%
January 1, 20137
 
0.2%
May 25, 20217
 
0.2%
May 4, 20216
 
0.2%
February 1, 20116
 
0.2%
Other values (268)494
 
13.9%
(Missing)2982
84.0%
2021-05-07T15:18:17.829640image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2012152
 
8.9%
2011142
 
8.3%
2013106
 
6.2%
202188
 
5.2%
may82
 
4.8%
april74
 
4.3%
173
 
4.3%
january56
 
3.3%
september56
 
3.3%
june55
 
3.2%
Other values (52)820
48.1%

Most occurring characters

ValueCountFrequency (%)
1136
14.0%
21018
12.6%
1950
11.7%
0626
 
7.7%
,566
 
7.0%
r386
 
4.8%
e349
 
4.3%
a296
 
3.7%
u294
 
3.6%
y231
 
2.9%
Other values (38)2244
27.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number3154
39.0%
Lowercase Letter2659
32.8%
Space Separator1136
 
14.0%
Uppercase Letter577
 
7.1%
Other Punctuation568
 
7.0%
Close Punctuation2
 
< 0.1%

Most frequent character per category

ValueCountFrequency (%)
r386
14.5%
e349
13.1%
a296
11.1%
u294
11.1%
y231
8.7%
b153
 
5.8%
t137
 
5.2%
p130
 
4.9%
l120
 
4.5%
n114
 
4.3%
Other values (9)449
16.9%
ValueCountFrequency (%)
J157
27.2%
M138
23.9%
A119
20.6%
S56
 
9.7%
F47
 
8.1%
O34
 
5.9%
D10
 
1.7%
N7
 
1.2%
B3
 
0.5%
C2
 
0.3%
Other values (4)4
 
0.7%
ValueCountFrequency (%)
21018
32.3%
1950
30.1%
0626
19.8%
3197
 
6.2%
4111
 
3.5%
571
 
2.3%
758
 
1.8%
852
 
1.6%
645
 
1.4%
926
 
0.8%
ValueCountFrequency (%)
,566
99.6%
;1
 
0.2%
/1
 
0.2%
ValueCountFrequency (%)
1136
100.0%
ValueCountFrequency (%)
)2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common4860
60.0%
Latin3236
40.0%

Most frequent character per script

ValueCountFrequency (%)
r386
 
11.9%
e349
 
10.8%
a296
 
9.1%
u294
 
9.1%
y231
 
7.1%
J157
 
4.9%
b153
 
4.7%
M138
 
4.3%
t137
 
4.2%
p130
 
4.0%
Other values (23)965
29.8%
ValueCountFrequency (%)
1136
23.4%
21018
20.9%
1950
19.5%
0626
12.9%
,566
11.6%
3197
 
4.1%
4111
 
2.3%
571
 
1.5%
758
 
1.2%
852
 
1.1%
Other values (5)75
 
1.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII8096
100.0%

Most frequent character per block

ValueCountFrequency (%)
1136
14.0%
21018
12.6%
1950
11.7%
0626
 
7.7%
,566
 
7.0%
r386
 
4.8%
e349
 
4.3%
a296
 
3.7%
u294
 
3.6%
y231
 
2.9%
Other values (38)2244
27.7%

publisher_id
Categorical

HIGH CARDINALITY
MISSING

Distinct257
Distinct (%)45.2%
Missing2982
Missing (%)84.0%
Memory size27.9 KiB
publisher138
 
14
publisher037
 
14
publisher289
 
13
publisher155
 
13
publisher032
 
12
Other values (252)
502 

Length

Max length12
Median length12
Mean length12
Min length12

Characters and Unicode

Total characters6816
Distinct characters19
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique165 ?
Unique (%)29.0%

Sample

1st rowpublisher149
2nd rowpublisher335
3rd rowpublisher381
4th rowpublisher099
5th rowpublisher184
ValueCountFrequency (%)
publisher13814
 
0.4%
publisher03714
 
0.4%
publisher28913
 
0.4%
publisher15513
 
0.4%
publisher03212
 
0.3%
publisher00412
 
0.3%
publisher37810
 
0.3%
publisher20310
 
0.3%
publisher26110
 
0.3%
publisher1059
 
0.3%
Other values (247)451
 
12.7%
(Missing)2982
84.0%
2021-05-07T15:18:18.054844image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
publisher13814
 
2.5%
publisher03714
 
2.5%
publisher28913
 
2.3%
publisher15513
 
2.3%
publisher00412
 
2.1%
publisher03212
 
2.1%
publisher26110
 
1.8%
publisher37810
 
1.8%
publisher20310
 
1.8%
publisher1059
 
1.6%
Other values (247)451
79.4%

Most occurring characters

ValueCountFrequency (%)
p568
 
8.3%
u568
 
8.3%
b568
 
8.3%
l568
 
8.3%
i568
 
8.3%
s568
 
8.3%
h568
 
8.3%
e568
 
8.3%
r568
 
8.3%
0283
 
4.2%
Other values (9)1421
20.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter5112
75.0%
Decimal Number1704
 
25.0%

Most frequent character per category

ValueCountFrequency (%)
0283
16.6%
3255
15.0%
1239
14.0%
2216
12.7%
7134
7.9%
8130
7.6%
9128
7.5%
4118
6.9%
5101
 
5.9%
6100
 
5.9%
ValueCountFrequency (%)
p568
11.1%
u568
11.1%
b568
11.1%
l568
11.1%
i568
11.1%
s568
11.1%
h568
11.1%
e568
11.1%
r568
11.1%

Most occurring scripts

ValueCountFrequency (%)
Latin5112
75.0%
Common1704
 
25.0%

Most frequent character per script

ValueCountFrequency (%)
0283
16.6%
3255
15.0%
1239
14.0%
2216
12.7%
7134
7.9%
8130
7.6%
9128
7.5%
4118
6.9%
5101
 
5.9%
6100
 
5.9%
ValueCountFrequency (%)
p568
11.1%
u568
11.1%
b568
11.1%
l568
11.1%
i568
11.1%
s568
11.1%
h568
11.1%
e568
11.1%
r568
11.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII6816
100.0%

Most frequent character per block

ValueCountFrequency (%)
p568
 
8.3%
u568
 
8.3%
b568
 
8.3%
l568
 
8.3%
i568
 
8.3%
s568
 
8.3%
h568
 
8.3%
e568
 
8.3%
r568
 
8.3%
0283
 
4.2%
Other values (9)1421
20.8%

reading_age
Categorical

MISSING

Distinct31
Distinct (%)24.6%
Missing3424
Missing (%)96.5%
Memory size27.9 KiB
18 years and up
14 
8 - 12 years
14 
14 years and up
12 
4 - 8 years
11 
13 years and up
10 
Other values (26)
65 

Length

Max length15
Median length13
Mean length13.23809524
Min length8

Characters and Unicode

Total characters1668
Distinct characters21
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique12 ?
Unique (%)9.5%

Sample

1st row14 years and up
2nd row14 years and up
3rd row8 - 12 years
4th row8 - 12 years
5th row12 - 17 years
ValueCountFrequency (%)
18 years and up14
 
0.4%
8 - 12 years14
 
0.4%
14 years and up12
 
0.3%
4 - 8 years11
 
0.3%
13 years and up10
 
0.3%
10 - 14 years7
 
0.2%
13 - 17 years7
 
0.2%
12 - 15 years7
 
0.2%
14 - 17 years6
 
0.2%
15 years and up4
 
0.1%
Other values (21)34
 
1.0%
(Missing)3424
96.5%
2021-05-07T15:18:18.254026image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
years126
25.1%
77
15.3%
and48
 
9.6%
up48
 
9.6%
1232
 
6.4%
828
 
5.6%
1425
 
5.0%
1320
 
4.0%
1818
 
3.6%
1717
 
3.4%
Other values (11)63
12.5%

Most occurring characters

ValueCountFrequency (%)
376
22.5%
a174
10.4%
1146
 
8.8%
y126
 
7.6%
e126
 
7.6%
r126
 
7.6%
s126
 
7.6%
-77
 
4.6%
n48
 
2.9%
d48
 
2.9%
Other values (11)295
17.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter870
52.2%
Space Separator376
22.5%
Decimal Number345
 
20.7%
Dash Punctuation77
 
4.6%

Most frequent character per category

ValueCountFrequency (%)
1146
42.3%
846
 
13.3%
438
 
11.0%
234
 
9.9%
326
 
7.5%
720
 
5.8%
516
 
4.6%
012
 
3.5%
66
 
1.7%
91
 
0.3%
ValueCountFrequency (%)
a174
20.0%
y126
14.5%
e126
14.5%
r126
14.5%
s126
14.5%
n48
 
5.5%
d48
 
5.5%
u48
 
5.5%
p48
 
5.5%
ValueCountFrequency (%)
376
100.0%
ValueCountFrequency (%)
-77
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin870
52.2%
Common798
47.8%

Most frequent character per script

ValueCountFrequency (%)
376
47.1%
1146
 
18.3%
-77
 
9.6%
846
 
5.8%
438
 
4.8%
234
 
4.3%
326
 
3.3%
720
 
2.5%
516
 
2.0%
012
 
1.5%
Other values (2)7
 
0.9%
ValueCountFrequency (%)
a174
20.0%
y126
14.5%
e126
14.5%
r126
14.5%
s126
14.5%
n48
 
5.5%
d48
 
5.5%
u48
 
5.5%
p48
 
5.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII1668
100.0%

Most frequent character per block

ValueCountFrequency (%)
376
22.5%
a174
10.4%
1146
 
8.8%
y126
 
7.6%
e126
 
7.6%
r126
 
7.6%
s126
 
7.6%
-77
 
4.6%
n48
 
2.9%
d48
 
2.9%
Other values (11)295
17.7%

lexile_measure
Categorical

HIGH CARDINALITY
MISSING
UNIFORM

Distinct61
Distinct (%)69.3%
Missing3462
Missing (%)97.5%
Memory size27.9 KiB
710L
 
5
850L
 
4
770L
 
3
HL710L
 
3
740L
 
3
Other values (56)
70 

Length

Max length6
Median length4
Mean length4.670454545
Min length2

Characters and Unicode

Total characters411
Distinct characters17
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique42 ?
Unique (%)47.7%

Sample

1st rowHL740L
2nd row760L
3rd row880L
4th row710L
5th row930
ValueCountFrequency (%)
710L5
 
0.1%
850L4
 
0.1%
770L3
 
0.1%
HL710L3
 
0.1%
740L3
 
0.1%
950L2
 
0.1%
760L2
 
0.1%
690L2
 
0.1%
HL740L2
 
0.1%
990L2
 
0.1%
Other values (51)60
 
1.7%
(Missing)3462
97.5%
2021-05-07T15:18:18.493243image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
710l5
 
5.7%
850l4
 
4.5%
770l3
 
3.4%
hl710l3
 
3.4%
740l3
 
3.4%
ad380l2
 
2.3%
760l2
 
2.3%
690l2
 
2.3%
6902
 
2.3%
920l2
 
2.3%
Other values (51)60
68.2%

Most occurring characters

ValueCountFrequency (%)
L98
23.8%
095
23.1%
739
 
9.5%
923
 
5.6%
622
 
5.4%
119
 
4.6%
H18
 
4.4%
518
 
4.4%
817
 
4.1%
413
 
3.2%
Other values (7)49
11.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number265
64.5%
Uppercase Letter146
35.5%

Most frequent character per category

ValueCountFrequency (%)
095
35.8%
739
14.7%
923
 
8.7%
622
 
8.3%
119
 
7.2%
518
 
6.8%
817
 
6.4%
413
 
4.9%
312
 
4.5%
27
 
2.6%
ValueCountFrequency (%)
L98
67.1%
H18
 
12.3%
A12
 
8.2%
D12
 
8.2%
N3
 
2.1%
G2
 
1.4%
P1
 
0.7%

Most occurring scripts

ValueCountFrequency (%)
Common265
64.5%
Latin146
35.5%

Most frequent character per script

ValueCountFrequency (%)
095
35.8%
739
14.7%
923
 
8.7%
622
 
8.3%
119
 
7.2%
518
 
6.8%
817
 
6.4%
413
 
4.9%
312
 
4.5%
27
 
2.6%
ValueCountFrequency (%)
L98
67.1%
H18
 
12.3%
A12
 
8.2%
D12
 
8.2%
N3
 
2.1%
G2
 
1.4%
P1
 
0.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII411
100.0%

Most frequent character per block

ValueCountFrequency (%)
L98
23.8%
095
23.1%
739
 
9.5%
923
 
5.6%
622
 
5.4%
119
 
4.6%
H18
 
4.4%
518
 
4.4%
817
 
4.1%
413
 
3.2%
Other values (7)49
11.9%

grade_level
Categorical

MISSING

Distinct25
Distinct (%)25.0%
Missing3450
Missing (%)97.2%
Memory size27.9 KiB
7 - 9
16 
3 - 7
14 
9 - 12
12 
Preschool - 3
10 
5 - 9
Other values (20)
41 

Length

Max length24
Median length6
Mean length7.33
Min length5

Characters and Unicode

Total characters733
Distinct characters29
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique9 ?
Unique (%)9.0%

Sample

1st row9 - 12
2nd row3 - 7
3rd row3 - 7
4th row7 - 9
5th row10 - 12
ValueCountFrequency (%)
7 - 916
 
0.5%
3 - 714
 
0.4%
9 - 1212
 
0.3%
Preschool - 310
 
0.3%
5 - 97
 
0.2%
8 and up6
 
0.2%
8 - 124
 
0.1%
10 and up3
 
0.1%
10 - 123
 
0.1%
8 - 93
 
0.1%
Other values (15)22
 
0.6%
(Missing)3450
97.2%
2021-05-07T15:18:18.701433image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
88
29.3%
941
13.7%
735
 
11.7%
325
 
8.3%
1221
 
7.0%
preschool17
 
5.7%
814
 
4.7%
and12
 
4.0%
up12
 
4.0%
511
 
3.7%
Other values (7)24
 
8.0%

Most occurring characters

ValueCountFrequency (%)
200
27.3%
-88
12.0%
941
 
5.6%
735
 
4.8%
o34
 
4.6%
132
 
4.4%
225
 
3.4%
325
 
3.4%
r23
 
3.1%
e23
 
3.1%
Other values (19)207
28.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter229
31.2%
Space Separator200
27.3%
Decimal Number196
26.7%
Dash Punctuation88
 
12.0%
Uppercase Letter20
 
2.7%

Most frequent character per category

ValueCountFrequency (%)
o34
14.8%
r23
10.0%
e23
10.0%
n18
7.9%
s17
7.4%
c17
7.4%
h17
7.4%
l17
7.4%
a15
6.6%
d15
6.6%
Other values (5)33
14.4%
ValueCountFrequency (%)
941
20.9%
735
17.9%
132
16.3%
225
12.8%
325
12.8%
814
 
7.1%
511
 
5.6%
06
 
3.1%
65
 
2.6%
42
 
1.0%
ValueCountFrequency (%)
P17
85.0%
K3
 
15.0%
ValueCountFrequency (%)
200
100.0%
ValueCountFrequency (%)
-88
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common484
66.0%
Latin249
34.0%

Most frequent character per script

ValueCountFrequency (%)
o34
13.7%
r23
9.2%
e23
9.2%
n18
 
7.2%
P17
 
6.8%
s17
 
6.8%
c17
 
6.8%
h17
 
6.8%
l17
 
6.8%
a15
 
6.0%
Other values (7)51
20.5%
ValueCountFrequency (%)
200
41.3%
-88
18.2%
941
 
8.5%
735
 
7.2%
132
 
6.6%
225
 
5.2%
325
 
5.2%
814
 
2.9%
511
 
2.3%
06
 
1.2%
Other values (2)7
 
1.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII733
100.0%

Most frequent character per block

ValueCountFrequency (%)
200
27.3%
-88
12.0%
941
 
5.6%
735
 
4.8%
o34
 
4.6%
132
 
4.4%
225
 
3.4%
325
 
3.4%
r23
 
3.1%
e23
 
3.1%
Other values (19)207
28.2%

weight
Real number (ℝ≥0)

MISSING

Distinct177
Distinct (%)34.1%
Missing3031
Missing (%)85.4%
Infinite0
Infinite (%)0.0%
Mean2372.200829
Minimum400.07
Maximum7212.11
Zeros0
Zeros (%)0.0%
Memory size27.9 KiB
2021-05-07T15:18:18.802525image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum400.07
5-th percentile493.966
Q1612.35
median839.15
Q34036.97
95-th percentile6626.976
Maximum7212.11
Range6812.04
Interquartile range (IQR)3424.62

Descriptive statistics

Standard deviation2232.405524
Coefficient of variation (CV)0.9410693636
Kurtosis-0.8572532392
Mean2372.200829
Median Absolute Deviation (MAD)340.2
Skewness0.8412088434
Sum1231172.23
Variance4983634.424
MonotocityNot monotonic
2021-05-07T15:18:18.901615image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
612.3517
 
0.5%
566.9915
 
0.4%
680.3915
 
0.4%
453.5914
 
0.4%
3628.7413
 
0.4%
5805.9812
 
0.3%
789.2512
 
0.3%
635.0311
 
0.3%
6894.610
 
0.3%
589.679
 
0.3%
Other values (167)391
 
11.0%
(Missing)3031
85.4%
ValueCountFrequency (%)
400.071
 
< 0.1%
453.5914
0.4%
462.663
 
0.1%
467.21
 
< 0.1%
471.742
 
0.1%
ValueCountFrequency (%)
7212.112
0.1%
7166.752
0.1%
7030.682
0.1%
6985.324
0.1%
6939.961
 
< 0.1%

rating_value_0
Real number (ℝ≥0)

ZEROS

Distinct171
Distinct (%)4.8%
Missing10
Missing (%)0.3%
Infinite0
Infinite (%)0.0%
Mean4.025016949
Minimum0
Maximum5
Zeros48
Zeros (%)1.4%
Memory size27.9 KiB
2021-05-07T15:18:19.009713image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile3.56
Q13.88
median4.08
Q34.26
95-th percentile4.53
Maximum5
Range5
Interquartile range (IQR)0.38

Descriptive statistics

Standard deviation0.5604124694
Coefficient of variation (CV)0.1392323254
Kurtosis33.84330361
Mean4.025016949
Median Absolute Deviation (MAD)0.19
Skewness-5.001519247
Sum14248.56
Variance0.3140621359
MonotocityNot monotonic
2021-05-07T15:18:19.116811image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4.1361
 
1.7%
4.0359
 
1.7%
4.2558
 
1.6%
4.258
 
1.6%
4.1256
 
1.6%
4.155
 
1.5%
4.0853
 
1.5%
4.2653
 
1.5%
4.0552
 
1.5%
4.0452
 
1.5%
Other values (161)2983
84.0%
ValueCountFrequency (%)
048
1.4%
11
 
< 0.1%
23
 
0.1%
3.031
 
< 0.1%
3.131
 
< 0.1%
ValueCountFrequency (%)
551
1.4%
4.913
 
0.1%
4.893
 
0.1%
4.882
 
0.1%
4.871
 
< 0.1%

rating_value_1
Real number (ℝ≥0)

MISSING

Distinct19
Distinct (%)3.4%
Missing2997
Missing (%)84.4%
Infinite0
Infinite (%)0.0%
Mean4.519529837
Minimum3.2
Maximum5
Zeros0
Zeros (%)0.0%
Memory size27.9 KiB
2021-05-07T15:18:19.209895image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum3.2
5-th percentile4
Q14.4
median4.6
Q34.7
95-th percentile4.9
Maximum5
Range1.8
Interquartile range (IQR)0.3

Descriptive statistics

Standard deviation0.2840859923
Coefficient of variation (CV)0.06285742157
Kurtosis2.147925241
Mean4.519529837
Median Absolute Deviation (MAD)0.2
Skewness-1.110406868
Sum2499.3
Variance0.08070485101
MonotocityNot monotonic
2021-05-07T15:18:19.290969image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
4.6101
 
2.8%
4.789
 
2.5%
4.582
 
2.3%
4.868
 
1.9%
4.451
 
1.4%
4.350
 
1.4%
525
 
0.7%
4.224
 
0.7%
4.118
 
0.5%
412
 
0.3%
Other values (9)33
 
0.9%
(Missing)2997
84.4%
ValueCountFrequency (%)
3.21
< 0.1%
3.31
< 0.1%
3.41
< 0.1%
3.51
< 0.1%
3.62
0.1%
ValueCountFrequency (%)
525
 
0.7%
4.910
 
0.3%
4.868
1.9%
4.789
2.5%
4.6101
2.8%

rating_count_0
Real number (ℝ≥0)

ZEROS

Distinct3136
Distinct (%)88.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean55045.40169
Minimum0
Maximum3803071
Zeros48
Zeros (%)1.4%
Memory size27.9 KiB
2021-05-07T15:18:19.395064image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile13.45
Q13565.5
median13081
Q342713
95-th percentile211299
Maximum3803071
Range3803071
Interquartile range (IQR)39147.5

Descriptive statistics

Standard deviation183651.4098
Coefficient of variation (CV)3.336362424
Kurtosis174.5681774
Mean55045.40169
Median Absolute Deviation (MAD)11907
Skewness11.37485349
Sum195411176
Variance3.372784032 × 1010
MonotocityNot monotonic
2021-05-07T15:18:19.501160image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
048
 
1.4%
143
 
1.2%
221
 
0.6%
313
 
0.4%
511
 
0.3%
98
 
0.2%
77
 
0.2%
46
 
0.2%
106
 
0.2%
115
 
0.1%
Other values (3126)3382
95.3%
ValueCountFrequency (%)
048
1.4%
143
1.2%
221
0.6%
313
 
0.4%
46
 
0.2%
ValueCountFrequency (%)
38030712
0.1%
30871771
 
< 0.1%
26788011
 
< 0.1%
24647673
0.1%
22750781
 
< 0.1%

rating_count_1
Real number (ℝ≥0)

Distinct477
Distinct (%)13.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean370.7591549
Minimum1
Maximum40409
Zeros0
Zeros (%)0.0%
Memory size27.9 KiB
2021-05-07T15:18:19.617266image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q31
95-th percentile1701.2
Maximum40409
Range40408
Interquartile range (IQR)0

Descriptive statistics

Standard deviation2046.63989
Coefficient of variation (CV)5.520133119
Kurtosis163.1039697
Mean370.7591549
Median Absolute Deviation (MAD)0
Skewness11.12509855
Sum1316195
Variance4188734.841
MonotocityNot monotonic
2021-05-07T15:18:19.725364image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
13002
84.6%
36
 
0.2%
155
 
0.1%
95
 
0.1%
74
 
0.1%
24
 
0.1%
603
 
0.1%
113
 
0.1%
1663
 
0.1%
4612
 
0.1%
Other values (467)513
 
14.5%
ValueCountFrequency (%)
13002
84.6%
24
 
0.1%
36
 
0.2%
41
 
< 0.1%
52
 
0.1%
ValueCountFrequency (%)
404091
< 0.1%
381071
< 0.1%
370592
0.1%
286591
< 0.1%
207651
< 0.1%

dimension_0
Real number (ℝ≥0)

MISSING

Distinct157
Distinct (%)30.7%
Missing3038
Missing (%)85.6%
Infinite0
Infinite (%)0.0%
Mean15.438125
Minimum1.47
Maximum30.48
Zeros0
Zeros (%)0.0%
Memory size27.9 KiB
2021-05-07T15:18:19.839468image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum1.47
5-th percentile10.67
Q113.97
median15.24
Q316.36
95-th percentile21.59
Maximum30.48
Range29.01
Interquartile range (IQR)2.39

Descriptive statistics

Standard deviation3.438329249
Coefficient of variation (CV)0.2227167644
Kurtosis6.005539709
Mean15.438125
Median Absolute Deviation (MAD)1.27
Skewness0.3747231861
Sum7904.32
Variance11.82210802
MonotocityNot monotonic
2021-05-07T15:18:19.934555image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
15.2459
 
1.7%
13.9750
 
1.4%
16.5130
 
0.8%
15.8822
 
0.6%
16.2611
 
0.3%
16.2111
 
0.3%
12.710
 
0.3%
15.579
 
0.3%
13.499
 
0.3%
14.619
 
0.3%
Other values (147)292
 
8.2%
(Missing)3038
85.6%
ValueCountFrequency (%)
1.471
 
< 0.1%
1.931
 
< 0.1%
2.543
0.1%
3.051
 
< 0.1%
3.171
 
< 0.1%
ValueCountFrequency (%)
30.481
< 0.1%
29.841
< 0.1%
29.211
< 0.1%
28.71
< 0.1%
28.652
0.1%

dimension_1
Real number (ℝ≥0)

MISSING

Distinct161
Distinct (%)31.4%
Missing3038
Missing (%)85.6%
Infinite0
Infinite (%)0.0%
Mean3.686113281
Minimum0.03
Maximum27.43
Zeros0
Zeros (%)0.0%
Memory size27.9 KiB
2021-05-07T15:18:20.037648image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0.03
5-th percentile0.9565
Q12.125
median2.79
Q33.58
95-th percentile14.381
Maximum27.43
Range27.4
Interquartile range (IQR)1.455

Descriptive statistics

Standard deviation4.004740578
Coefficient of variation (CV)1.086439909
Kurtosis13.44920999
Mean3.686113281
Median Absolute Deviation (MAD)0.76
Skewness3.647769293
Sum1887.29
Variance16.03794709
MonotocityNot monotonic
2021-05-07T15:18:20.132735image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2.5441
 
1.2%
3.8121
 
0.6%
2.7918
 
0.5%
3.1718
 
0.5%
3.315
 
0.4%
1.2714
 
0.4%
4.4514
 
0.4%
1.5211
 
0.3%
2.2910
 
0.3%
3.5610
 
0.3%
Other values (151)340
 
9.6%
(Missing)3038
85.6%
ValueCountFrequency (%)
0.031
< 0.1%
0.231
< 0.1%
0.411
< 0.1%
0.462
0.1%
0.512
0.1%
ValueCountFrequency (%)
27.431
 
< 0.1%
23.52
0.1%
22.862
0.1%
21.593
0.1%
21.341
 
< 0.1%

dimension_2
Real number (ℝ≥0)

MISSING

Distinct141
Distinct (%)28.3%
Missing3051
Missing (%)85.9%
Infinite0
Infinite (%)0.0%
Mean22.09118236
Minimum1.02
Maximum28.91
Zeros0
Zeros (%)0.0%
Memory size27.9 KiB
2021-05-07T15:18:20.238832image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum1.02
5-th percentile17.2
Q120.95
median22.86
Q324.13
95-th percentile25.65
Maximum28.91
Range27.89
Interquartile range (IQR)3.18

Descriptive statistics

Standard deviation3.236897097
Coefficient of variation (CV)0.1465243935
Kurtosis14.72254391
Mean22.09118236
Median Absolute Deviation (MAD)1.3
Skewness-2.883913001
Sum11023.5
Variance10.47750282
MonotocityNot monotonic
2021-05-07T15:18:20.337921image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
22.8653
 
1.5%
20.9532
 
0.9%
23.531
 
0.9%
24.1331
 
0.9%
21.5924
 
0.7%
20.3217
 
0.5%
24.2110
 
0.3%
21.7410
 
0.3%
25.410
 
0.3%
22.239
 
0.3%
Other values (131)272
 
7.7%
(Missing)3051
85.9%
ValueCountFrequency (%)
1.021
< 0.1%
2.541
< 0.1%
2.91
< 0.1%
3.051
< 0.1%
3.171
< 0.1%
ValueCountFrequency (%)
28.911
 
< 0.1%
28.731
 
< 0.1%
28.573
0.1%
27.941
 
< 0.1%
27.641
 
< 0.1%

genre_0
Categorical

HIGH CARDINALITY
MISSING

Distinct80
Distinct (%)2.4%
Missing150
Missing (%)4.2%
Memory size27.9 KiB
Nonfiction
564 
Fantasy
408 
Fiction
297 
Romance
205 
Poetry
176 
Other values (75)
1750 

Length

Max length20
Median length8
Mean length9.396176471
Min length3

Characters and Unicode

Total characters31947
Distinct characters43
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique26 ?
Unique (%)0.8%

Sample

1st rowHistorical Fiction
2nd rowMemoir
3rd rowNonfiction
4th rowFantasy
5th rowHistory
ValueCountFrequency (%)
Nonfiction564
15.9%
Fantasy408
11.5%
Fiction297
 
8.4%
Romance205
 
5.8%
Poetry176
 
5.0%
Young Adult171
 
4.8%
Historical Fiction170
 
4.8%
Picture Books149
 
4.2%
Science Fiction141
 
4.0%
Graphic Novels128
 
3.6%
Other values (70)991
27.9%
(Missing)150
 
4.2%
2021-05-07T15:18:20.566129image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
fiction612
14.1%
nonfiction564
 
13.0%
fantasy450
 
10.4%
romance227
 
5.2%
poetry176
 
4.1%
historical175
 
4.0%
science174
 
4.0%
adult174
 
4.0%
young171
 
3.9%
books149
 
3.4%
Other values (73)1471
33.9%

Most occurring characters

ValueCountFrequency (%)
o4014
12.6%
i3563
 
11.2%
n2898
 
9.1%
t2596
 
8.1%
c2250
 
7.0%
a1735
 
5.4%
r1693
 
5.3%
e1524
 
4.8%
s1367
 
4.3%
F1082
 
3.4%
Other values (33)9225
28.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter26652
83.4%
Uppercase Letter4352
 
13.6%
Space Separator943
 
3.0%

Most frequent character per category

ValueCountFrequency (%)
o4014
15.1%
i3563
13.4%
n2898
10.9%
t2596
9.7%
c2250
8.4%
a1735
6.5%
r1693
6.4%
e1524
 
5.7%
s1367
 
5.1%
y1007
 
3.8%
Other values (12)4005
15.0%
ValueCountFrequency (%)
F1082
24.9%
N694
15.9%
H414
 
9.5%
P360
 
8.3%
M280
 
6.4%
R230
 
5.3%
S216
 
5.0%
G201
 
4.6%
B190
 
4.4%
C184
 
4.2%
Other values (10)501
11.5%
ValueCountFrequency (%)
943
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin31004
97.0%
Common943
 
3.0%

Most frequent character per script

ValueCountFrequency (%)
o4014
12.9%
i3563
11.5%
n2898
 
9.3%
t2596
 
8.4%
c2250
 
7.3%
a1735
 
5.6%
r1693
 
5.5%
e1524
 
4.9%
s1367
 
4.4%
F1082
 
3.5%
Other values (32)8282
26.7%
ValueCountFrequency (%)
943
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII31947
100.0%

Most frequent character per block

ValueCountFrequency (%)
o4014
12.6%
i3563
 
11.2%
n2898
 
9.1%
t2596
 
8.1%
c2250
 
7.0%
a1735
 
5.4%
r1693
 
5.3%
e1524
 
4.8%
s1367
 
4.3%
F1082
 
3.4%
Other values (33)9225
28.9%

genre_1
Categorical

HIGH CARDINALITY
MISSING

Distinct123
Distinct (%)3.7%
Missing197
Missing (%)5.5%
Memory size27.9 KiB
Fiction
495 
Nonfiction
341 
Young Adult
246 
Contemporary
225 
Memoir
 
169
Other values (118)
1877 

Length

Max length20
Median length8
Mean length8.974053087
Min length3

Characters and Unicode

Total characters30090
Distinct characters48
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique30 ?
Unique (%)0.9%

Sample

1st rowFiction
2nd rowHistory
3rd rowUrban Fantasy
4th rowNonfiction
5th rowTrue Crime
ValueCountFrequency (%)
Fiction495
 
13.9%
Nonfiction341
 
9.6%
Young Adult246
 
6.9%
Contemporary225
 
6.3%
Memoir169
 
4.8%
Romance136
 
3.8%
Fantasy123
 
3.5%
Childrens94
 
2.6%
Historical Fiction89
 
2.5%
Mystery88
 
2.5%
Other values (113)1347
37.9%
(Missing)197
 
5.5%
2021-05-07T15:18:20.802344image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
fiction673
16.3%
nonfiction341
 
8.3%
adult301
 
7.3%
young248
 
6.0%
contemporary242
 
5.9%
romance175
 
4.2%
memoir169
 
4.1%
fantasy155
 
3.8%
science108
 
2.6%
historical108
 
2.6%
Other values (116)1602
38.9%

Most occurring characters

ValueCountFrequency (%)
i3459
 
11.5%
o3405
 
11.3%
n2643
 
8.8%
t2216
 
7.4%
c1786
 
5.9%
r1727
 
5.7%
e1624
 
5.4%
a1354
 
4.5%
l979
 
3.3%
s973
 
3.2%
Other values (38)9924
33.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter25048
83.2%
Uppercase Letter4273
 
14.2%
Space Separator769
 
2.6%

Most frequent character per category

ValueCountFrequency (%)
F904
21.2%
C532
12.5%
N430
10.1%
M385
9.0%
A329
 
7.7%
H324
 
7.6%
Y248
 
5.8%
R230
 
5.4%
S191
 
4.5%
G154
 
3.6%
Other values (14)546
12.8%
ValueCountFrequency (%)
i3459
13.8%
o3405
13.6%
n2643
10.6%
t2216
8.8%
c1786
 
7.1%
r1727
 
6.9%
e1624
 
6.5%
a1354
 
5.4%
l979
 
3.9%
s973
 
3.9%
Other values (13)4882
19.5%
ValueCountFrequency (%)
769
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin29321
97.4%
Common769
 
2.6%

Most frequent character per script

ValueCountFrequency (%)
i3459
 
11.8%
o3405
 
11.6%
n2643
 
9.0%
t2216
 
7.6%
c1786
 
6.1%
r1727
 
5.9%
e1624
 
5.5%
a1354
 
4.6%
l979
 
3.3%
s973
 
3.3%
Other values (37)9155
31.2%
ValueCountFrequency (%)
769
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII30090
100.0%

Most frequent character per block

ValueCountFrequency (%)
i3459
 
11.5%
o3405
 
11.3%
n2643
 
8.8%
t2216
 
7.4%
c1786
 
5.9%
r1727
 
5.7%
e1624
 
5.4%
a1354
 
4.5%
l979
 
3.3%
s973
 
3.2%
Other values (38)9924
33.0%

genre_2
Categorical

HIGH CARDINALITY
MISSING

Distinct199
Distinct (%)6.0%
Missing228
Missing (%)6.4%
Memory size27.9 KiB
Fiction
347 
Contemporary
 
221
Romance
 
152
Fantasy
 
117
Audiobook
 
112
Other values (194)
2373 

Length

Max length24
Median length8
Mean length8.766706803
Min length3

Characters and Unicode

Total characters29123
Distinct characters50
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique55 ?
Unique (%)1.7%

Sample

1st rowHistorical
2nd rowPolitics
3rd rowFiction
4th rowRussia
5th rowCrime
ValueCountFrequency (%)
Fiction347
 
9.8%
Contemporary221
 
6.2%
Romance152
 
4.3%
Fantasy117
 
3.3%
Audiobook112
 
3.2%
Nonfiction111
 
3.1%
Biography110
 
3.1%
Young Adult97
 
2.7%
Historical94
 
2.6%
Humor92
 
2.6%
Other values (189)1869
52.6%
(Missing)228
 
6.4%
2021-05-07T15:18:21.031553image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
fiction513
 
12.9%
contemporary254
 
6.4%
romance198
 
5.0%
fantasy158
 
4.0%
adult144
 
3.6%
historical135
 
3.4%
biography115
 
2.9%
audiobook112
 
2.8%
nonfiction111
 
2.8%
young105
 
2.6%
Other values (187)2123
53.5%

Most occurring characters

ValueCountFrequency (%)
o3215
 
11.0%
i2960
 
10.2%
r2107
 
7.2%
n1981
 
6.8%
t1883
 
6.5%
a1837
 
6.3%
e1639
 
5.6%
c1476
 
5.1%
s1068
 
3.7%
y1062
 
3.6%
Other values (40)9895
34.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter24267
83.3%
Uppercase Letter4208
 
14.4%
Space Separator646
 
2.2%
Decimal Number2
 
< 0.1%

Most frequent character per category

ValueCountFrequency (%)
F794
18.9%
C479
11.4%
H394
9.4%
A371
8.8%
M279
 
6.6%
R260
 
6.2%
B242
 
5.8%
T217
 
5.2%
P179
 
4.3%
S172
 
4.1%
Other values (15)821
19.5%
ValueCountFrequency (%)
o3215
13.2%
i2960
12.2%
r2107
8.7%
n1981
 
8.2%
t1883
 
7.8%
a1837
 
7.6%
e1639
 
6.8%
c1476
 
6.1%
s1068
 
4.4%
y1062
 
4.4%
Other values (12)5039
20.8%
ValueCountFrequency (%)
21
50.0%
11
50.0%
ValueCountFrequency (%)
646
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin28475
97.8%
Common648
 
2.2%

Most frequent character per script

ValueCountFrequency (%)
o3215
 
11.3%
i2960
 
10.4%
r2107
 
7.4%
n1981
 
7.0%
t1883
 
6.6%
a1837
 
6.5%
e1639
 
5.8%
c1476
 
5.2%
s1068
 
3.8%
y1062
 
3.7%
Other values (37)9247
32.5%
ValueCountFrequency (%)
646
99.7%
21
 
0.2%
11
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII29123
100.0%

Most frequent character per block

ValueCountFrequency (%)
o3215
 
11.0%
i2960
 
10.2%
r2107
 
7.2%
n1981
 
6.8%
t1883
 
6.5%
a1837
 
6.3%
e1639
 
5.6%
c1476
 
5.1%
s1068
 
3.7%
y1062
 
3.6%
Other values (40)9895
34.0%

genre_3
Categorical

HIGH CARDINALITY
MISSING

Distinct233
Distinct (%)7.1%
Missing248
Missing (%)7.0%
Memory size27.9 KiB
Fiction
278 
Audiobook
260 
Romance
 
120
Biography
 
120
Fantasy
 
113
Other values (228)
2411 

Length

Max length24
Median length9
Mean length9.167777105
Min length3

Characters and Unicode

Total characters30272
Distinct characters51
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique60 ?
Unique (%)1.8%

Sample

1st rowAudiobook
2nd rowRace
3rd rowMagic
4th rowBiography
5th rowAudiobook
ValueCountFrequency (%)
Fiction278
 
7.8%
Audiobook260
 
7.3%
Romance120
 
3.4%
Biography120
 
3.4%
Fantasy113
 
3.2%
Contemporary99
 
2.8%
Adult89
 
2.5%
Historical81
 
2.3%
Nonfiction78
 
2.2%
Young Adult69
 
1.9%
Other values (223)1995
56.2%
(Missing)248
 
7.0%
2021-05-07T15:18:21.253755image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
fiction474
 
11.4%
audiobook260
 
6.2%
adult240
 
5.7%
fantasy196
 
4.7%
romance161
 
3.9%
contemporary141
 
3.4%
biography137
 
3.3%
historical122
 
2.9%
mystery118
 
2.8%
thriller100
 
2.4%
Other values (232)2225
53.3%

Most occurring characters

ValueCountFrequency (%)
o3253
 
10.7%
i3083
 
10.2%
t1953
 
6.5%
a1844
 
6.1%
r1844
 
6.1%
n1798
 
5.9%
e1732
 
5.7%
c1426
 
4.7%
l1171
 
3.9%
s1161
 
3.8%
Other values (41)11007
36.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter25064
82.8%
Uppercase Letter4330
 
14.3%
Space Separator872
 
2.9%
Decimal Number6
 
< 0.1%

Most frequent character per category

ValueCountFrequency (%)
F793
18.3%
A638
14.7%
C373
8.6%
H320
 
7.4%
M300
 
6.9%
R265
 
6.1%
B233
 
5.4%
T192
 
4.4%
S190
 
4.4%
N173
 
4.0%
Other values (14)853
19.7%
ValueCountFrequency (%)
o3253
13.0%
i3083
12.3%
t1953
 
7.8%
a1844
 
7.4%
r1844
 
7.4%
n1798
 
7.2%
e1732
 
6.9%
c1426
 
5.7%
l1171
 
4.7%
s1161
 
4.6%
Other values (14)5799
23.1%
ValueCountFrequency (%)
23
50.0%
13
50.0%
ValueCountFrequency (%)
872
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin29394
97.1%
Common878
 
2.9%

Most frequent character per script

ValueCountFrequency (%)
o3253
 
11.1%
i3083
 
10.5%
t1953
 
6.6%
a1844
 
6.3%
r1844
 
6.3%
n1798
 
6.1%
e1732
 
5.9%
c1426
 
4.9%
l1171
 
4.0%
s1161
 
3.9%
Other values (38)10129
34.5%
ValueCountFrequency (%)
872
99.3%
23
 
0.3%
13
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII30272
100.0%

Most frequent character per block

ValueCountFrequency (%)
o3253
 
10.7%
i3083
 
10.2%
t1953
 
6.5%
a1844
 
6.1%
r1844
 
6.1%
n1798
 
5.9%
e1732
 
5.7%
c1426
 
4.7%
l1171
 
3.9%
s1161
 
3.8%
Other values (41)11007
36.4%

genre_4
Categorical

HIGH CARDINALITY
MISSING

Distinct261
Distinct (%)8.0%
Missing280
Missing (%)7.9%
Memory size27.9 KiB
Audiobook
290 
Fiction
 
240
Adult
 
144
Biography
 
90
Contemporary
 
76
Other values (256)
2430 

Length

Max length28
Median length9
Mean length9.516819572
Min length3

Characters and Unicode

Total characters31120
Distinct characters51
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique75 ?
Unique (%)2.3%

Sample

1st rowRomance
2nd rowSocial Justice
3rd rowParanormal
4th rowHistorical
5th rowHistory
ValueCountFrequency (%)
Audiobook290
 
8.2%
Fiction240
 
6.8%
Adult144
 
4.1%
Biography90
 
2.5%
Contemporary76
 
2.1%
Mystery Thriller73
 
2.1%
Romance71
 
2.0%
Fantasy70
 
2.0%
Historical66
 
1.9%
Young Adult53
 
1.5%
Other values (251)2097
59.1%
(Missing)280
 
7.9%
2021-05-07T15:18:21.486967image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
fiction443
 
10.4%
audiobook290
 
6.8%
adult286
 
6.7%
fantasy170
 
4.0%
biography139
 
3.3%
romance136
 
3.2%
contemporary127
 
3.0%
mystery121
 
2.8%
thriller105
 
2.5%
young94
 
2.2%
Other values (257)2349
55.1%

Most occurring characters

ValueCountFrequency (%)
o3167
 
10.2%
i2975
 
9.6%
e2083
 
6.7%
t2005
 
6.4%
r1886
 
6.1%
a1864
 
6.0%
n1711
 
5.5%
c1426
 
4.6%
l1289
 
4.1%
s1152
 
3.7%
Other values (41)11562
37.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter25771
82.8%
Uppercase Letter4351
 
14.0%
Space Separator990
 
3.2%
Decimal Number8
 
< 0.1%

Most frequent character per category

ValueCountFrequency (%)
A772
17.7%
F724
16.6%
M357
8.2%
C339
7.8%
S297
 
6.8%
H289
 
6.6%
R285
 
6.6%
B220
 
5.1%
T185
 
4.3%
P156
 
3.6%
Other values (15)727
16.7%
ValueCountFrequency (%)
o3167
12.3%
i2975
11.5%
e2083
 
8.1%
t2005
 
7.8%
r1886
 
7.3%
a1864
 
7.2%
n1711
 
6.6%
c1426
 
5.5%
l1289
 
5.0%
s1152
 
4.5%
Other values (13)6213
24.1%
ValueCountFrequency (%)
24
50.0%
14
50.0%
ValueCountFrequency (%)
990
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin30122
96.8%
Common998
 
3.2%

Most frequent character per script

ValueCountFrequency (%)
o3167
 
10.5%
i2975
 
9.9%
e2083
 
6.9%
t2005
 
6.7%
r1886
 
6.3%
a1864
 
6.2%
n1711
 
5.7%
c1426
 
4.7%
l1289
 
4.3%
s1152
 
3.8%
Other values (38)10564
35.1%
ValueCountFrequency (%)
990
99.2%
24
 
0.4%
14
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII31120
100.0%

Most frequent character per block

ValueCountFrequency (%)
o3167
 
10.2%
i2975
 
9.6%
e2083
 
6.7%
t2005
 
6.4%
r1886
 
6.1%
a1864
 
6.0%
n1711
 
5.5%
c1426
 
4.6%
l1289
 
4.1%
s1152
 
3.7%
Other values (41)11562
37.2%

genre_5
Categorical

HIGH CARDINALITY
MISSING

Distinct287
Distinct (%)8.9%
Missing310
Missing (%)8.7%
Memory size27.9 KiB
Audiobook
341 
Adult
 
205
Fiction
 
174
Biography Memoir
 
73
Historical
 
60
Other values (282)
2387 

Length

Max length28
Median length9
Mean length9.719753086
Min length2

Characters and Unicode

Total characters31492
Distinct characters53
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique77 ?
Unique (%)2.4%

Sample

1st rowBooks About Books
2nd rowAudiobook
3rd rowAudiobook
4th rowRussian History
5th rowMystery
ValueCountFrequency (%)
Audiobook341
 
9.6%
Adult205
 
5.8%
Fiction174
 
4.9%
Biography Memoir73
 
2.1%
Historical60
 
1.7%
Contemporary54
 
1.5%
Romance52
 
1.5%
Biography50
 
1.4%
Adventure49
 
1.4%
Suspense48
 
1.4%
Other values (277)2134
60.1%
(Missing)310
 
8.7%
2021-05-07T15:18:21.723181image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
fiction374
 
8.6%
adult355
 
8.2%
audiobook341
 
7.9%
fantasy159
 
3.7%
biography123
 
2.8%
romance115
 
2.7%
contemporary107
 
2.5%
memoir103
 
2.4%
young94
 
2.2%
science84
 
1.9%
Other values (276)2482
57.2%

Most occurring characters

ValueCountFrequency (%)
o3206
 
10.2%
i2900
 
9.2%
e2101
 
6.7%
t1950
 
6.2%
a1845
 
5.9%
r1828
 
5.8%
n1644
 
5.2%
l1299
 
4.1%
c1292
 
4.1%
u1252
 
4.0%
Other values (43)12175
38.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter25948
82.4%
Uppercase Letter4435
 
14.1%
Space Separator1097
 
3.5%
Decimal Number12
 
< 0.1%

Most frequent character per category

ValueCountFrequency (%)
A919
20.7%
F660
14.9%
C389
8.8%
M324
 
7.3%
S315
 
7.1%
H247
 
5.6%
R236
 
5.3%
B222
 
5.0%
P146
 
3.3%
T144
 
3.2%
Other values (14)833
18.8%
ValueCountFrequency (%)
o3206
12.4%
i2900
11.2%
e2101
 
8.1%
t1950
 
7.5%
a1845
 
7.1%
r1828
 
7.0%
n1644
 
6.3%
l1299
 
5.0%
c1292
 
5.0%
u1252
 
4.8%
Other values (13)6631
25.6%
ValueCountFrequency (%)
15
41.7%
24
33.3%
91
 
8.3%
01
 
8.3%
81
 
8.3%
ValueCountFrequency (%)
1097
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin30383
96.5%
Common1109
 
3.5%

Most frequent character per script

ValueCountFrequency (%)
o3206
 
10.6%
i2900
 
9.5%
e2101
 
6.9%
t1950
 
6.4%
a1845
 
6.1%
r1828
 
6.0%
n1644
 
5.4%
l1299
 
4.3%
c1292
 
4.3%
u1252
 
4.1%
Other values (37)11066
36.4%
ValueCountFrequency (%)
1097
98.9%
15
 
0.5%
24
 
0.4%
91
 
0.1%
01
 
0.1%
81
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII31492
100.0%

Most frequent character per block

ValueCountFrequency (%)
o3206
 
10.2%
i2900
 
9.2%
e2101
 
6.7%
t1950
 
6.2%
a1845
 
5.9%
r1828
 
5.8%
n1644
 
5.2%
l1299
 
4.1%
c1292
 
4.1%
u1252
 
4.0%
Other values (43)12175
38.7%

genre_6
Categorical

HIGH CARDINALITY
MISSING

Distinct299
Distinct (%)9.3%
Missing338
Missing (%)9.5%
Memory size27.9 KiB
Audiobook
325 
Adult
254 
Fiction
 
116
Biography Memoir
 
79
Adult Fiction
 
72
Other values (294)
2366 

Length

Max length28
Median length9
Mean length9.646948941
Min length3

Characters and Unicode

Total characters30986
Distinct characters53
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique80 ?
Unique (%)2.5%

Sample

1st rowAdult
2nd rowSociology
3rd rowVampires
4th rowAudiobook
5th rowAdult
ValueCountFrequency (%)
Audiobook325
 
9.2%
Adult254
 
7.2%
Fiction116
 
3.3%
Biography Memoir79
 
2.2%
Adult Fiction72
 
2.0%
Adventure58
 
1.6%
Contemporary55
 
1.5%
Historical54
 
1.5%
Fantasy52
 
1.5%
Romance49
 
1.4%
Other values (289)2098
59.1%
(Missing)338
 
9.5%
2021-05-07T15:18:21.956393image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
adult416
 
9.7%
fiction335
 
7.8%
audiobook325
 
7.6%
fantasy166
 
3.9%
biography128
 
3.0%
contemporary102
 
2.4%
romance102
 
2.4%
memoir94
 
2.2%
science82
 
1.9%
young80
 
1.9%
Other values (283)2464
57.4%

Most occurring characters

ValueCountFrequency (%)
o3059
 
9.9%
i2817
 
9.1%
e2022
 
6.5%
t1993
 
6.4%
a1806
 
5.8%
r1729
 
5.6%
n1597
 
5.2%
l1369
 
4.4%
u1321
 
4.3%
c1248
 
4.0%
Other values (43)12025
38.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter25533
82.4%
Uppercase Letter4357
 
14.1%
Space Separator1082
 
3.5%
Decimal Number14
 
< 0.1%

Most frequent character per category

ValueCountFrequency (%)
A1005
23.1%
F641
14.7%
C355
 
8.1%
S313
 
7.2%
M300
 
6.9%
H235
 
5.4%
R216
 
5.0%
B214
 
4.9%
T158
 
3.6%
P143
 
3.3%
Other values (15)777
17.8%
ValueCountFrequency (%)
o3059
12.0%
i2817
11.0%
e2022
 
7.9%
t1993
 
7.8%
a1806
 
7.1%
r1729
 
6.8%
n1597
 
6.3%
l1369
 
5.4%
u1321
 
5.2%
c1248
 
4.9%
Other values (14)6572
25.7%
ValueCountFrequency (%)
17
50.0%
25
35.7%
92
 
14.3%
ValueCountFrequency (%)
1082
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin29890
96.5%
Common1096
 
3.5%

Most frequent character per script

ValueCountFrequency (%)
o3059
 
10.2%
i2817
 
9.4%
e2022
 
6.8%
t1993
 
6.7%
a1806
 
6.0%
r1729
 
5.8%
n1597
 
5.3%
l1369
 
4.6%
u1321
 
4.4%
c1248
 
4.2%
Other values (39)10929
36.6%
ValueCountFrequency (%)
1082
98.7%
17
 
0.6%
25
 
0.5%
92
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII30986
100.0%

Most frequent character per block

ValueCountFrequency (%)
o3059
 
9.9%
i2817
 
9.1%
e2022
 
6.5%
t1993
 
6.4%
a1806
 
5.8%
r1729
 
5.6%
n1597
 
5.2%
l1369
 
4.4%
u1321
 
4.3%
c1248
 
4.0%
Other values (43)12025
38.8%

genre_7
Categorical

HIGH CARDINALITY
MISSING

Distinct325
Distinct (%)10.2%
Missing378
Missing (%)10.6%
Memory size27.9 KiB
Adult
263 
Audiobook
 
231
Adult Fiction
 
97
Fiction
 
88
Biography Memoir
 
59
Other values (320)
2434 

Length

Max length30
Median length9
Mean length9.589848676
Min length2

Characters and Unicode

Total characters30419
Distinct characters56
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique88 ?
Unique (%)2.8%

Sample

1st rowAdult Fiction
2nd rowAnti Racist
3rd rowMystery
4th rowPolitics
5th rowJournalism
ValueCountFrequency (%)
Adult263
 
7.4%
Audiobook231
 
6.5%
Adult Fiction97
 
2.7%
Fiction88
 
2.5%
Biography Memoir59
 
1.7%
Adventure58
 
1.6%
Romance56
 
1.6%
Fantasy49
 
1.4%
Contemporary48
 
1.4%
Magic48
 
1.4%
Other values (315)2175
61.3%
(Missing)378
 
10.6%
2021-05-07T15:18:22.185602image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
adult441
 
10.3%
fiction344
 
8.1%
audiobook231
 
5.4%
fantasy167
 
3.9%
romance129
 
3.0%
biography95
 
2.2%
contemporary92
 
2.2%
science85
 
2.0%
memoir80
 
1.9%
young69
 
1.6%
Other values (316)2535
59.4%

Most occurring characters

ValueCountFrequency (%)
o2769
 
9.1%
i2717
 
8.9%
e2120
 
7.0%
t2007
 
6.6%
a1841
 
6.1%
n1756
 
5.8%
r1705
 
5.6%
l1361
 
4.5%
u1289
 
4.2%
c1258
 
4.1%
Other values (46)11596
38.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter24975
82.1%
Uppercase Letter4335
 
14.3%
Space Separator1096
 
3.6%
Decimal Number13
 
< 0.1%

Most frequent character per category

ValueCountFrequency (%)
A974
22.5%
F617
14.2%
C364
 
8.4%
S311
 
7.2%
M260
 
6.0%
H235
 
5.4%
R229
 
5.3%
B221
 
5.1%
T164
 
3.8%
P133
 
3.1%
Other values (15)827
19.1%
ValueCountFrequency (%)
o2769
11.1%
i2717
10.9%
e2120
 
8.5%
t2007
 
8.0%
a1841
 
7.4%
n1756
 
7.0%
r1705
 
6.8%
l1361
 
5.4%
u1289
 
5.2%
c1258
 
5.0%
Other values (15)6152
24.6%
ValueCountFrequency (%)
16
46.2%
23
23.1%
92
 
15.4%
01
 
7.7%
61
 
7.7%
ValueCountFrequency (%)
1096
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin29310
96.4%
Common1109
 
3.6%

Most frequent character per script

ValueCountFrequency (%)
o2769
 
9.4%
i2717
 
9.3%
e2120
 
7.2%
t2007
 
6.8%
a1841
 
6.3%
n1756
 
6.0%
r1705
 
5.8%
l1361
 
4.6%
u1289
 
4.4%
c1258
 
4.3%
Other values (40)10487
35.8%
ValueCountFrequency (%)
1096
98.8%
16
 
0.5%
23
 
0.3%
92
 
0.2%
01
 
0.1%
61
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII30417
> 99.9%
None2
 
< 0.1%

Most frequent character per block

ValueCountFrequency (%)
o2769
 
9.1%
i2717
 
8.9%
e2120
 
7.0%
t2007
 
6.6%
a1841
 
6.1%
n1756
 
5.8%
r1705
 
5.6%
l1361
 
4.5%
u1289
 
4.2%
c1258
 
4.1%
Other values (45)11594
38.1%
ValueCountFrequency (%)
é2
100.0%

genre_8
Categorical

HIGH CARDINALITY
MISSING

Distinct343
Distinct (%)10.9%
Missing414
Missing (%)11.7%
Memory size27.9 KiB
Audiobook
249 
Adult
 
230
Adult Fiction
 
91
Contemporary
 
69
Adventure
 
68
Other values (338)
2429 

Length

Max length28
Median length9
Mean length9.723852041
Min length2

Characters and Unicode

Total characters30494
Distinct characters58
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique112 ?
Unique (%)3.6%

Sample

1st rowBritish Literature
2nd rowAmerican History
3rd rowSupernatural
4th rowEuropean History
5th rowSociology
ValueCountFrequency (%)
Audiobook249
 
7.0%
Adult230
 
6.5%
Adult Fiction91
 
2.6%
Contemporary69
 
1.9%
Adventure68
 
1.9%
Biography Memoir62
 
1.7%
Magic53
 
1.5%
Fiction47
 
1.3%
Historical46
 
1.3%
Supernatural43
 
1.2%
Other values (333)2178
61.4%
(Missing)414
 
11.7%
2021-05-07T15:18:22.401799image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
adult433
 
10.1%
fiction280
 
6.5%
audiobook249
 
5.8%
fantasy148
 
3.4%
contemporary104
 
2.4%
romance90
 
2.1%
biography85
 
2.0%
young84
 
2.0%
memoir83
 
1.9%
science78
 
1.8%
Other values (328)2671
62.0%

Most occurring characters

ValueCountFrequency (%)
o2855
 
9.4%
i2688
 
8.8%
e2051
 
6.7%
t2014
 
6.6%
a1824
 
6.0%
r1755
 
5.8%
n1603
 
5.3%
l1392
 
4.6%
u1376
 
4.5%
c1188
 
3.9%
Other values (48)11748
38.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter24973
81.9%
Uppercase Letter4332
 
14.2%
Space Separator1169
 
3.8%
Decimal Number20
 
0.1%

Most frequent character per category

ValueCountFrequency (%)
o2855
11.4%
i2688
10.8%
e2051
 
8.2%
t2014
 
8.1%
a1824
 
7.3%
r1755
 
7.0%
n1603
 
6.4%
l1392
 
5.6%
u1376
 
5.5%
c1188
 
4.8%
Other values (16)6227
24.9%
ValueCountFrequency (%)
A996
23.0%
F546
12.6%
C389
 
9.0%
S316
 
7.3%
M271
 
6.3%
H237
 
5.5%
B217
 
5.0%
R175
 
4.0%
T152
 
3.5%
L133
 
3.1%
Other values (15)900
20.8%
ValueCountFrequency (%)
111
55.0%
93
 
15.0%
23
 
15.0%
71
 
5.0%
01
 
5.0%
61
 
5.0%
ValueCountFrequency (%)
1169
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin29305
96.1%
Common1189
 
3.9%

Most frequent character per script

ValueCountFrequency (%)
o2855
 
9.7%
i2688
 
9.2%
e2051
 
7.0%
t2014
 
6.9%
a1824
 
6.2%
r1755
 
6.0%
n1603
 
5.5%
l1392
 
4.8%
u1376
 
4.7%
c1188
 
4.1%
Other values (41)10559
36.0%
ValueCountFrequency (%)
1169
98.3%
111
 
0.9%
93
 
0.3%
23
 
0.3%
71
 
0.1%
01
 
0.1%
61
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII30492
> 99.9%
None2
 
< 0.1%

Most frequent character per block

ValueCountFrequency (%)
o2855
 
9.4%
i2688
 
8.8%
e2051
 
6.7%
t2014
 
6.6%
a1824
 
6.0%
r1755
 
5.8%
n1603
 
5.3%
l1392
 
4.6%
u1376
 
4.5%
c1188
 
3.9%
Other values (47)11746
38.5%
ValueCountFrequency (%)
é2
100.0%

genre_9
Categorical

HIGH CARDINALITY
MISSING

Distinct349
Distinct (%)11.3%
Missing450
Missing (%)12.7%
Memory size27.9 KiB
Adult
 
211
Audiobook
 
194
Adult Fiction
 
121
Family
 
62
Novels
 
60
Other values (344)
2452 

Length

Max length30
Median length9
Mean length9.820967742
Min length2

Characters and Unicode

Total characters30445
Distinct characters56
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique107 ?
Unique (%)3.5%

Sample

1st rowChick Lit
2nd rowAfrican American
3rd rowFae
4th rowRomanovs
5th rowBiography
ValueCountFrequency (%)
Adult211
 
5.9%
Audiobook194
 
5.5%
Adult Fiction121
 
3.4%
Family62
 
1.7%
Novels60
 
1.7%
Adventure59
 
1.7%
Contemporary55
 
1.5%
Fiction50
 
1.4%
Supernatural48
 
1.4%
Science Fiction Fantasy47
 
1.3%
Other values (339)2193
61.8%
(Missing)450
 
12.7%
2021-05-07T15:18:22.986330image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
adult429
 
10.0%
fiction347
 
8.1%
audiobook194
 
4.5%
fantasy137
 
3.2%
science113
 
2.6%
contemporary87
 
2.0%
romance83
 
1.9%
young80
 
1.9%
novels76
 
1.8%
american66
 
1.5%
Other values (334)2693
62.6%

Most occurring characters

ValueCountFrequency (%)
i2694
 
8.8%
o2564
 
8.4%
e2218
 
7.3%
t2081
 
6.8%
a1852
 
6.1%
n1760
 
5.8%
r1714
 
5.6%
l1502
 
4.9%
u1344
 
4.4%
c1299
 
4.3%
Other values (46)11417
37.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter24897
81.8%
Uppercase Letter4305
 
14.1%
Space Separator1205
 
4.0%
Decimal Number38
 
0.1%

Most frequent character per category

ValueCountFrequency (%)
i2694
10.8%
o2564
10.3%
e2218
 
8.9%
t2081
 
8.4%
a1852
 
7.4%
n1760
 
7.1%
r1714
 
6.9%
l1502
 
6.0%
u1344
 
5.4%
c1299
 
5.2%
Other values (15)5869
23.6%
ValueCountFrequency (%)
A971
22.6%
F619
14.4%
C367
 
8.5%
S354
 
8.2%
M267
 
6.2%
H215
 
5.0%
R170
 
3.9%
T158
 
3.7%
B153
 
3.6%
N136
 
3.2%
Other values (14)895
20.8%
ValueCountFrequency (%)
121
55.3%
97
 
18.4%
25
 
13.2%
63
 
7.9%
01
 
2.6%
81
 
2.6%
ValueCountFrequency (%)
1205
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin29202
95.9%
Common1243
 
4.1%

Most frequent character per script

ValueCountFrequency (%)
i2694
 
9.2%
o2564
 
8.8%
e2218
 
7.6%
t2081
 
7.1%
a1852
 
6.3%
n1760
 
6.0%
r1714
 
5.9%
l1502
 
5.1%
u1344
 
4.6%
c1299
 
4.4%
Other values (39)10174
34.8%
ValueCountFrequency (%)
1205
96.9%
121
 
1.7%
97
 
0.6%
25
 
0.4%
63
 
0.2%
01
 
0.1%
81
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII30445
100.0%

Most frequent character per block

ValueCountFrequency (%)
i2694
 
8.8%
o2564
 
8.4%
e2218
 
7.3%
t2081
 
6.8%
a1852
 
6.1%
n1760
 
5.8%
r1714
 
5.6%
l1502
 
4.9%
u1344
 
4.4%
c1299
 
4.3%
Other values (46)11417
37.5%

genre_0_weight
Real number (ℝ≥0)

MISSING

Distinct85
Distinct (%)2.5%
Missing150
Missing (%)4.2%
Infinite0
Infinite (%)0.0%
Mean0.4274176471
Minimum0.02
Maximum1
Zeros0
Zeros (%)0.0%
Memory size27.9 KiB
2021-05-07T15:18:23.087422image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0.02
5-th percentile0.24
Q10.31
median0.39
Q30.5
95-th percentile0.8
Maximum1
Range0.98
Interquartile range (IQR)0.19

Descriptive statistics

Standard deviation0.1652967474
Coefficient of variation (CV)0.3867335579
Kurtosis2.276902158
Mean0.4274176471
Median Absolute Deviation (MAD)0.09
Skewness1.457267827
Sum1453.22
Variance0.02732301469
MonotocityNot monotonic
2021-05-07T15:18:23.194520image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.3125
 
3.5%
0.33124
 
3.5%
0.35123
 
3.5%
0.36118
 
3.3%
0.38115
 
3.2%
0.37114
 
3.2%
0.31110
 
3.1%
0.28109
 
3.1%
0.32104
 
2.9%
0.39103
 
2.9%
Other values (75)2255
63.5%
(Missing)150
 
4.2%
ValueCountFrequency (%)
0.021
 
< 0.1%
0.172
 
0.1%
0.183
 
0.1%
0.1912
0.3%
0.215
0.4%
ValueCountFrequency (%)
147
1.3%
0.992
 
0.1%
0.985
 
0.1%
0.972
 
0.1%
0.965
 
0.1%

genre_1_weight
Real number (ℝ≥0)

MISSING

Distinct43
Distinct (%)1.3%
Missing197
Missing (%)5.5%
Infinite0
Infinite (%)0.0%
Mean0.1983745899
Minimum0.01
Maximum0.6
Zeros0
Zeros (%)0.0%
Memory size27.9 KiB
2021-05-07T15:18:23.296613image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0.01
5-th percentile0.08
Q10.15
median0.2
Q30.24
95-th percentile0.31
Maximum0.6
Range0.59
Interquartile range (IQR)0.09

Descriptive statistics

Standard deviation0.07047029519
Coefficient of variation (CV)0.3552385173
Kurtosis0.8161536546
Mean0.1983745899
Median Absolute Deviation (MAD)0.04
Skewness0.1355569219
Sum665.15
Variance0.004966062504
MonotocityNot monotonic
2021-05-07T15:18:23.397704image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=43)
ValueCountFrequency (%)
0.18216
 
6.1%
0.19206
 
5.8%
0.2202
 
5.7%
0.17195
 
5.5%
0.21194
 
5.5%
0.16178
 
5.0%
0.22175
 
4.9%
0.24172
 
4.8%
0.15168
 
4.7%
0.23157
 
4.4%
Other values (33)1490
42.0%
(Missing)197
 
5.5%
ValueCountFrequency (%)
0.016
 
0.2%
0.0231
0.9%
0.0321
0.6%
0.0427
0.8%
0.0515
0.4%
ValueCountFrequency (%)
0.61
 
< 0.1%
0.56
0.2%
0.411
 
< 0.1%
0.47
0.2%
0.394
0.1%

genre_2_weight
Real number (ℝ≥0)

MISSING

Distinct29
Distinct (%)0.9%
Missing228
Missing (%)6.4%
Infinite0
Infinite (%)0.0%
Mean0.1191240217
Minimum0.01
Maximum0.33
Zeros0
Zeros (%)0.0%
Memory size27.9 KiB
2021-05-07T15:18:23.500798image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0.01
5-th percentile0.05
Q10.09
median0.12
Q30.15
95-th percentile0.2
Maximum0.33
Range0.32
Interquartile range (IQR)0.06

Descriptive statistics

Standard deviation0.04648224918
Coefficient of variation (CV)0.3902004695
Kurtosis0.06063189243
Mean0.1191240217
Median Absolute Deviation (MAD)0.03
Skewness0.2478584788
Sum395.73
Variance0.002160599489
MonotocityNot monotonic
2021-05-07T15:18:23.586877image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%)
0.11296
 
8.3%
0.13268
 
7.5%
0.1260
 
7.3%
0.14257
 
7.2%
0.12249
 
7.0%
0.08247
 
7.0%
0.09247
 
7.0%
0.15221
 
6.2%
0.07196
 
5.5%
0.16162
 
4.6%
Other values (19)919
25.9%
(Missing)228
 
6.4%
ValueCountFrequency (%)
0.0127
 
0.8%
0.0244
1.2%
0.0333
 
0.9%
0.0444
1.2%
0.0585
2.4%
ValueCountFrequency (%)
0.331
 
< 0.1%
0.292
 
0.1%
0.272
 
0.1%
0.266
0.2%
0.2510
0.3%

genre_3_weight
Real number (ℝ≥0)

MISSING

Distinct23
Distinct (%)0.7%
Missing248
Missing (%)7.0%
Infinite0
Infinite (%)0.0%
Mean0.0779345851
Minimum0
Maximum0.25
Zeros4
Zeros (%)0.1%
Memory size27.9 KiB
2021-05-07T15:18:23.676959image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.03
Q10.06
median0.08
Q30.1
95-th percentile0.13
Maximum0.25
Range0.25
Interquartile range (IQR)0.04

Descriptive statistics

Standard deviation0.0316683141
Coefficient of variation (CV)0.406344809
Kurtosis0.421718351
Mean0.0779345851
Median Absolute Deviation (MAD)0.02
Skewness0.4179230961
Sum257.34
Variance0.001002882118
MonotocityNot monotonic
2021-05-07T15:18:23.767040image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
0.07433
12.2%
0.06419
11.8%
0.08379
10.7%
0.05353
9.9%
0.09344
9.7%
0.1300
8.5%
0.11241
6.8%
0.04207
5.8%
0.12148
 
4.2%
0.03111
 
3.1%
Other values (13)367
10.3%
(Missing)248
7.0%
ValueCountFrequency (%)
04
 
0.1%
0.0154
 
1.5%
0.0263
 
1.8%
0.03111
3.1%
0.04207
5.8%
ValueCountFrequency (%)
0.251
 
< 0.1%
0.221
 
< 0.1%
0.21
 
< 0.1%
0.192
 
0.1%
0.186
0.2%

genre_4_weight
Real number (ℝ≥0)

MISSING

Distinct16
Distinct (%)0.5%
Missing280
Missing (%)7.9%
Infinite0
Infinite (%)0.0%
Mean0.05497553517
Minimum0
Maximum0.15
Zeros6
Zeros (%)0.2%
Memory size27.9 KiB
2021-05-07T15:18:23.850116image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.02
Q10.04
median0.05
Q30.07
95-th percentile0.09
Maximum0.15
Range0.15
Interquartile range (IQR)0.03

Descriptive statistics

Standard deviation0.02263971639
Coefficient of variation (CV)0.4118143884
Kurtosis0.2051080207
Mean0.05497553517
Median Absolute Deviation (MAD)0.01
Skewness0.4033680049
Sum179.77
Variance0.0005125567583
MonotocityNot monotonic
2021-05-07T15:18:23.931190image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
0.05630
17.7%
0.04570
16.1%
0.06519
14.6%
0.07387
10.9%
0.03308
8.7%
0.08261
7.4%
0.09177
 
5.0%
0.02152
 
4.3%
0.01100
 
2.8%
0.190
 
2.5%
Other values (6)76
 
2.1%
(Missing)280
7.9%
ValueCountFrequency (%)
06
 
0.2%
0.01100
 
2.8%
0.02152
 
4.3%
0.03308
8.7%
0.04570
16.1%
ValueCountFrequency (%)
0.151
 
< 0.1%
0.142
 
0.1%
0.139
 
0.3%
0.1217
0.5%
0.1141
1.2%

genre_5_weight
Real number (ℝ≥0)

MISSING

Distinct13
Distinct (%)0.4%
Missing310
Missing (%)8.7%
Infinite0
Infinite (%)0.0%
Mean0.04166049383
Minimum0
Maximum0.12
Zeros12
Zeros (%)0.3%
Memory size27.9 KiB
2021-05-07T15:18:24.016267image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.01
Q10.03
median0.04
Q30.05
95-th percentile0.07
Maximum0.12
Range0.12
Interquartile range (IQR)0.02

Descriptive statistics

Standard deviation0.01697997061
Coefficient of variation (CV)0.4075796769
Kurtosis0.1911828658
Mean0.04166049383
Median Absolute Deviation (MAD)0.01
Skewness0.35054229
Sum134.98
Variance0.000288319402
MonotocityNot monotonic
2021-05-07T15:18:24.106349image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
0.04813
22.9%
0.03671
18.9%
0.05608
17.1%
0.06352
9.9%
0.02324
 
9.1%
0.07200
 
5.6%
0.01155
 
4.4%
0.0880
 
2.3%
0.0916
 
0.5%
012
 
0.3%
Other values (3)9
 
0.3%
(Missing)310
 
8.7%
ValueCountFrequency (%)
012
 
0.3%
0.01155
 
4.4%
0.02324
 
9.1%
0.03671
18.9%
0.04813
22.9%
ValueCountFrequency (%)
0.121
 
< 0.1%
0.112
 
0.1%
0.16
 
0.2%
0.0916
 
0.5%
0.0880
2.3%

genre_6_weight
Real number (ℝ≥0)

MISSING

Distinct12
Distinct (%)0.4%
Missing338
Missing (%)9.5%
Infinite0
Infinite (%)0.0%
Mean0.0326992528
Minimum0
Maximum0.11
Zeros15
Zeros (%)0.4%
Memory size27.9 KiB
2021-05-07T15:18:24.195430image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.01
Q10.02
median0.03
Q30.04
95-th percentile0.06
Maximum0.11
Range0.11
Interquartile range (IQR)0.02

Descriptive statistics

Standard deviation0.01345717081
Coefficient of variation (CV)0.4115436795
Kurtosis0.7881371657
Mean0.0326992528
Median Absolute Deviation (MAD)0.01
Skewness0.4850117625
Sum105.03
Variance0.0001810954463
MonotocityNot monotonic
2021-05-07T15:18:24.270498image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
0.031028
29.0%
0.04742
20.9%
0.02637
17.9%
0.05370
 
10.4%
0.01252
 
7.1%
0.06121
 
3.4%
0.0735
 
1.0%
015
 
0.4%
0.088
 
0.2%
0.12
 
0.1%
Other values (2)2
 
0.1%
(Missing)338
 
9.5%
ValueCountFrequency (%)
015
 
0.4%
0.01252
 
7.1%
0.02637
17.9%
0.031028
29.0%
0.04742
20.9%
ValueCountFrequency (%)
0.111
 
< 0.1%
0.12
 
0.1%
0.091
 
< 0.1%
0.088
 
0.2%
0.0735
1.0%

genre_7_weight
Real number (ℝ≥0)

MISSING

Distinct8
Distinct (%)0.3%
Missing378
Missing (%)10.6%
Infinite0
Infinite (%)0.0%
Mean0.02643442623
Minimum0
Maximum0.07
Zeros31
Zeros (%)0.9%
Memory size27.9 KiB
2021-05-07T15:18:24.348569image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.01
Q10.02
median0.03
Q30.03
95-th percentile0.04
Maximum0.07
Range0.07
Interquartile range (IQR)0.01

Descriptive statistics

Standard deviation0.01100977692
Coefficient of variation (CV)0.4164938866
Kurtosis0.4071740444
Mean0.02643442623
Median Absolute Deviation (MAD)0.01
Skewness0.4081915436
Sum83.85
Variance0.0001212151878
MonotocityNot monotonic
2021-05-07T15:18:24.421636image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
0.021055
29.7%
0.031053
29.7%
0.04474
13.4%
0.01403
 
11.4%
0.05128
 
3.6%
031
 
0.9%
0.0619
 
0.5%
0.079
 
0.3%
(Missing)378
 
10.6%
ValueCountFrequency (%)
031
 
0.9%
0.01403
 
11.4%
0.021055
29.7%
0.031053
29.7%
0.04474
13.4%
ValueCountFrequency (%)
0.079
 
0.3%
0.0619
 
0.5%
0.05128
 
3.6%
0.04474
13.4%
0.031053
29.7%

genre_8_weight
Real number (ℝ≥0)

MISSING
ZEROS

Distinct8
Distinct (%)0.3%
Missing414
Missing (%)11.7%
Infinite0
Infinite (%)0.0%
Mean0.021875
Minimum0
Maximum0.07
Zeros51
Zeros (%)1.4%
Memory size27.9 KiB
2021-05-07T15:18:24.503711image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.01
Q10.02
median0.02
Q30.03
95-th percentile0.04
Maximum0.07
Range0.07
Interquartile range (IQR)0.01

Descriptive statistics

Standard deviation0.009473794967
Coefficient of variation (CV)0.4330877699
Kurtosis0.539611845
Mean0.021875
Median Absolute Deviation (MAD)0.01
Skewness0.4431676516
Sum68.6
Variance8.975279107 × 105
MonotocityNot monotonic
2021-05-07T15:18:24.579780image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
0.021353
38.1%
0.03827
23.3%
0.01666
18.8%
0.04195
 
5.5%
051
 
1.4%
0.0538
 
1.1%
0.065
 
0.1%
0.071
 
< 0.1%
(Missing)414
 
11.7%
ValueCountFrequency (%)
051
 
1.4%
0.01666
18.8%
0.021353
38.1%
0.03827
23.3%
0.04195
 
5.5%
ValueCountFrequency (%)
0.071
 
< 0.1%
0.065
 
0.1%
0.0538
 
1.1%
0.04195
 
5.5%
0.03827
23.3%

genre_9_weight
Real number (ℝ≥0)

MISSING
ZEROS

Distinct7
Distinct (%)0.2%
Missing450
Missing (%)12.7%
Infinite0
Infinite (%)0.0%
Mean0.01817419355
Minimum0
Maximum0.06
Zeros69
Zeros (%)1.9%
Memory size27.9 KiB
2021-05-07T15:18:24.663856image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.01
Q10.01
median0.02
Q30.02
95-th percentile0.03
Maximum0.06
Range0.06
Interquartile range (IQR)0.01

Descriptive statistics

Standard deviation0.008106780423
Coefficient of variation (CV)0.4460599807
Kurtosis0.6149792219
Mean0.01817419355
Median Absolute Deviation (MAD)0.01
Skewness0.5045010848
Sum56.34
Variance6.571988883 × 105
MonotocityNot monotonic
2021-05-07T15:18:24.736923image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
0.021459
41.1%
0.011045
29.4%
0.03447
 
12.6%
0.0471
 
2.0%
069
 
1.9%
0.058
 
0.2%
0.061
 
< 0.1%
(Missing)450
 
12.7%
ValueCountFrequency (%)
069
 
1.9%
0.011045
29.4%
0.021459
41.1%
0.03447
 
12.6%
0.0471
 
2.0%
ValueCountFrequency (%)
0.061
 
< 0.1%
0.058
 
0.2%
0.0471
 
2.0%
0.03447
 
12.6%
0.021459
41.1%

price
Real number (ℝ≥0)

MISSING

Distinct377
Distinct (%)69.4%
Missing3007
Missing (%)84.7%
Infinite0
Infinite (%)0.0%
Mean231296.7624
Minimum0
Maximum978395
Zeros1
Zeros (%)< 0.1%
Memory size27.9 KiB
2021-05-07T15:18:24.839015image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile72041
Q1128922.5
median212946
Q3287224
95-th percentile529982.6
Maximum978395
Range978395
Interquartile range (IQR)158301.5

Descriptive statistics

Standard deviation138233.5085
Coefficient of variation (CV)0.5976456698
Kurtosis2.937813855
Mean231296.7624
Median Absolute Deviation (MAD)83012
Skewness1.401843799
Sum125594142
Variance1.910850287 × 1010
MonotocityNot monotonic
2021-05-07T15:18:24.950117image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
7204111
 
0.3%
14422611
 
0.3%
23084810
 
0.3%
142939
 
0.3%
2164119
 
0.3%
1153528
 
0.2%
1222816
 
0.2%
1297896
 
0.2%
431676
 
0.2%
864785
 
0.1%
Other values (367)462
 
13.0%
(Missing)3007
84.7%
ValueCountFrequency (%)
01
 
< 0.1%
142939
0.3%
287302
 
0.1%
425891
 
< 0.1%
431676
0.2%
ValueCountFrequency (%)
9783951
< 0.1%
8081831
< 0.1%
7485581
< 0.1%
7258921
< 0.1%
6928321
< 0.1%

Interactions

2021-05-07T15:17:32.609505image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-07T15:17:32.737622image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-07T15:17:32.835711image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-07T15:17:32.936802image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-07T15:17:33.043900image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-07T15:17:33.152999image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-07T15:17:33.245083image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-07T15:17:33.337167image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-07T15:17:33.431252image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-07T15:17:33.531344image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-07T15:17:33.634437image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-07T15:17:33.736530image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-07T15:17:33.835620image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-07T15:17:33.940716image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-07T15:17:34.042809image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-07T15:17:34.142900image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-07T15:17:34.248996image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-07T15:17:34.355093image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-07T15:17:34.457186image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-07T15:17:34.551271image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-07T15:17:34.648360image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-07T15:17:34.745448image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-07T15:17:34.849543image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-07T15:17:34.951636image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-07T15:17:35.058733image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-07T15:17:35.229889image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-07T15:17:35.326977image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-07T15:17:35.426067image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-07T15:17:35.520153image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-07T15:17:35.617241image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-07T15:17:35.717332image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-07T15:17:35.817423image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-07T15:17:35.918515image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-07T15:17:36.015604image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-07T15:17:36.110690image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-07T15:17:36.213784image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-07T15:17:36.312874image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-07T15:17:36.408961image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-07T15:17:36.512055image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-07T15:17:36.609143image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-07T15:17:36.705231image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-07T15:17:36.807324image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-07T15:17:36.911418image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-07T15:17:37.018516image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-07T15:17:37.110600image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-07T15:17:37.202684image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-07T15:17:37.300773image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-07T15:17:37.397861image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-07T15:17:37.495950image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-07T15:17:37.606050image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-07T15:17:37.712147image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-07T15:17:37.824248image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-07T15:17:37.932347image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-07T15:17:38.037442image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-07T15:17:38.149544image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-07T15:17:38.262647image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-07T15:17:38.366742image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-07T15:17:38.460827image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-07T15:17:38.562920image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-07T15:17:38.667015image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-07T15:17:38.864195image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-07T15:17:38.974294image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-07T15:17:39.088399image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-07T15:17:39.190491image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-07T15:17:39.293585image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-07T15:17:39.397680image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-07T15:17:39.500773image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-07T15:17:39.606870image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-07T15:17:39.709964image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-07T15:17:39.817061image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-07T15:17:39.930164image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-07T15:17:40.035260image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-07T15:17:40.139355image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-07T15:17:40.249454image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-07T15:17:40.356552image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-07T15:17:40.462649image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-07T15:17:40.565742image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-07T15:17:40.673841image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-07T15:17:40.776934image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-07T15:17:40.882030image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-07T15:17:40.991129image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-07T15:17:41.107235image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-07T15:17:41.205324image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-07T15:17:41.305415image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-07T15:17:41.406507image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-07T15:17:41.514605image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-07T15:17:41.623705image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-07T15:17:41.733805image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-07T15:17:41.841903image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-07T15:17:41.955006image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-07T15:17:42.065106image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-07T15:17:42.172204image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-07T15:17:42.285306image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-07T15:17:42.399410image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-07T15:17:42.508509image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-07T15:17:42.612604image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-07T15:17:42.722704image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-07T15:17:42.833805image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-07T15:17:42.940903image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-07T15:17:43.055007image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-07T15:17:43.170111image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-07T15:17:43.275207image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-07T15:17:43.495407image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-07T15:17:43.603506image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-07T15:17:43.712605image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-07T15:17:43.824707image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-07T15:17:43.936809image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-07T15:17:44.044908image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-07T15:17:44.163014image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-07T15:17:44.276117image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-07T15:17:44.385216image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-07T15:17:44.501322image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-07T15:17:44.616427image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-07T15:17:44.727528image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-07T15:17:44.835626image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-07T15:17:44.926709image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-07T15:17:45.022797image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-07T15:17:45.114880image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-07T15:17:45.212970image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-07T15:17:45.312060image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-07T15:17:45.414152image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-07T15:17:45.507237image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-07T15:17:45.601323image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-07T15:17:45.691405image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-07T15:17:45.783489image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-07T15:17:45.878575image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-07T15:17:45.973662image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-07T15:17:46.068748image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-07T15:17:46.159831image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-07T15:17:46.251915image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-07T15:17:46.350004image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-07T15:17:46.444089image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-07T15:17:46.535172image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-07T15:17:46.634263image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-07T15:17:46.725345image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-07T15:17:46.821433image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-07T15:17:46.912516image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-07T15:17:47.010605image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-07T15:17:47.107693image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-07T15:17:47.209786image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
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2021-05-07T15:17:47.395956image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-07T15:17:47.485036image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-07T15:17:47.577120image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-07T15:17:47.671206image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
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2021-05-07T15:17:48.049550image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
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2021-05-07T15:17:49.068477image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
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2021-05-07T15:17:56.951648image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
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2021-05-07T15:17:58.946462image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-07T15:17:59.061567image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
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2021-05-07T15:18:01.763024image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
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2021-05-07T15:18:02.780950image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-07T15:18:02.886046image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-07T15:18:02.992142image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-07T15:18:03.084226image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-07T15:18:03.175309image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
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2021-05-07T15:18:03.796875image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-07T15:18:03.900969image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-07T15:18:04.005064image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-07T15:18:04.107157image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-07T15:18:04.210250image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-07T15:18:04.314345image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-07T15:18:04.414436image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-07T15:18:04.504518image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-07T15:18:04.611616image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-07T15:18:04.714710image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-07T15:18:04.826811image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-07T15:18:04.935910image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-07T15:18:05.050014image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-07T15:18:05.165119image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-07T15:18:05.265210image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-07T15:18:05.365301image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-07T15:18:05.468395image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-07T15:18:05.571489image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-07T15:18:05.686594image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-07T15:18:05.792690image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-07T15:18:05.904792image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-07T15:18:06.016894image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-07T15:18:06.125993image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-07T15:18:06.232090image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-07T15:18:06.344192image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-07T15:18:06.453291image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-07T15:18:06.556385image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-07T15:18:06.663482image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-07T15:18:06.763573image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-07T15:18:06.874674image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-07T15:18:06.979770image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-07T15:18:07.093874image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-07T15:18:07.207978image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-07T15:18:07.304065image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-07T15:18:07.400152image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-07T15:18:07.498241image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-07T15:18:07.601335image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-07T15:18:07.715439image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-07T15:18:07.820534image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-07T15:18:07.932637image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-07T15:18:08.044738image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-07T15:18:08.153838image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-07T15:18:08.258934image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-07T15:18:08.371035image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-07T15:18:08.479134image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-07T15:18:08.577223image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-07T15:18:08.680317image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-07T15:18:08.775403image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-07T15:18:08.879498image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-07T15:18:08.980590image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-07T15:18:09.089689image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-07T15:18:09.200790image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-07T15:18:09.292874image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-07T15:18:09.384958image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-07T15:18:09.480044image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-07T15:18:09.579134image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-07T15:18:09.690235image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-07T15:18:09.792328image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-07T15:18:09.901428image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-07T15:18:10.008525image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-07T15:18:10.113621image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-07T15:18:10.216714image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-07T15:18:10.325814image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-07T15:18:10.432911image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-07T15:18:10.526996image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-07T15:18:10.620081image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-07T15:18:10.723175image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-07T15:18:10.818261image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-07T15:18:10.919353image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-07T15:18:11.019444image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-07T15:18:11.125541image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-07T15:18:11.224632image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-07T15:18:11.324722image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-07T15:18:11.426815image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-07T15:18:11.518899image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-07T15:18:11.613986image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-07T15:18:11.714077image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-07T15:18:11.812165image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-07T15:18:11.909254image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-07T15:18:12.003340image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-07T15:18:12.098426image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-07T15:18:12.199518image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-07T15:18:12.301611image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Correlations

2021-05-07T15:18:25.065221image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2021-05-07T15:18:25.310444image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2021-05-07T15:18:25.553666image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2021-05-07T15:18:25.807897image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.
2021-05-07T15:18:26.063129image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.

Missing values

2021-05-07T15:18:12.560847image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
A simple visualization of nullity by column.
2021-05-07T15:18:13.973131image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2021-05-07T15:18:14.884961image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.
2021-05-07T15:18:16.226181image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
The dendrogram allows you to more fully correlate variable completion, revealing trends deeper than the pairwise ones visible in the correlation heatmap.

Sample

First rows

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0author2106Just after the Second World War, in the small English village of Chawton, an unusual but like-minded group of people band together to attempt something remarkable.One hundred and fifty years ago, Chawton was the final home of Jane Austen, one of England's finest novelists. Now it's home to a few distant relatives and their diminishing estate. With the last bit of Austen's legacy threatened, a group of disparate individuals come together to preserve both Jane Austen's home and her legacy. These people—a laborer, a young widow, the local doctor, and a movie star, among others—could not be more different and yet they are united in their love for the works and words of Austen. As each of them endures their own quiet struggle with loss and trauma, some from the recent war, others from more distant tragedies, they rally together to create the Jane Austen Society.HardcoverNaN309.0NaNNaNNaNNaNNaNNaN3.76NaN266251NaNNaNNaNHistorical FictionFictionHistoricalAudiobookRomanceBooks About BooksAdultAdult FictionBritish LiteratureChick Lit0.450.220.080.060.050.040.030.020.020.02NaN
1author1018Blame it on Hawaii’s rainbows, sparkling beaches, fruity cocktails, and sensuous breezes. For Heather Diamond, there for a summer course on China, a sea change began when romance bloomed with Fred, an ethnomusicologist from Hong Kong.One night under a full moon, Fred tells Heather the story of Chang’e, the moon goddess. He points out how the shadows form a rabbit pounding an elixir of immortality, but all Heather sees in the moon is a man’s face.Returning to her teaching job in Texas, Heather wonders if the whirlwind affair was a moment of madness. She is, after all, forty-five years old, married, a mother and grandmother.Rabbit in the Moon follows Heather and Fred’s relationship as well as Heather’s challenges with multiple mid-life reinventions, such as moving to Hawaii, entering a Ph.D. program, and living in a dorm with students half her age.When Fred goes on sabbatical, Heather finds herself on the Hong Kong island of Cheung Chau with his large, boisterous family. For an independent, reserved American, adjusting to his extended family isn’t easy. She wants to fit in, but is culture shocked by the lack of privacy, the language barrier, and the Chinese aesthetic of renao (“hot & noisy”).Life on Cheung Chau is overwhelming but also wondrous. Heather chronicles family celebrations, ancestor rituals, and a rich cycle of festivals like the Hungry Ghosts Festival, Chinese New Year, and the Bun Festival. Her descriptions of daily life and traditions are exquisite, seamlessly combining the insights of an ethnographer with the fascination of a curious newcomer who gradually transitions topart of the family.Ultimately, Heather’s experiences abroad make her realize what she has overlooked with her own family back in the United States, and she sets about making amends.Moving between Hawaii, Hong Kong, and the continental US, Rabbit in the Moon is an honest, finely crafted meditation on intercultural marriage, the importance of family, and finding the courage to follow your dreams.PaperbackNaNNaNNaNNaNNaNNaNNaNNaN4.48NaN211NaNNaNNaNMemoirNaNNaNNaNNaNNaNNaNNaNNaNNaN1.00NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
2author1087The Pulitzer Prize–winning, bestselling author of The Warmth of Other Suns examines the unspoken caste system that has shaped America and shows how our lives today are still defined by a hierarchy of human divisions.“As we go about our daily lives, caste is the wordless usher in a darkened theater, flashlight cast down in the aisles, guiding us to our assigned seats for a performance. The hierarchy of caste is not about feelings or morality. It is about power—which groups have it and which do not.”In this brilliant book, Isabel Wilkerson gives us a masterful portrait of an unseen phenomenon in America as she explores, through an immersive, deeply researched narrative and stories about real people, how America today and throughout its history has been shaped by a hidden caste system, a rigid hierarchy of human rankings.Beyond race, class, or other factors, there is a powerful caste system that influences people’s lives and behavior and the nation’s fate. Linking the caste systems of America, India, and Nazi Germany, Wilkerson explores eight pillars that underlie caste systems across civilizations, including divine will, bloodlines, stigma, and more. Using riveting stories about people—including Martin Luther King, Jr., baseball’s Satchel Paige, a single father and his toddler son, Wilkerson herself, and many others—she shows the ways that the insidious undertow of caste is experienced every day. She documents how the Nazis studied the racial systems in America to plan their out-cast of the Jews; she discusses why the cruel logic of caste requires that there be a bottom rung for those in the middle to measure themselves against; she writes about the surprising health costs of caste, in depression and life expectancy, and the effects of this hierarchy on our culture and politics. Finally, she points forward to ways America can move beyond the artificial and destructive separations of human divisions, toward hope in our common humanity.HardcoverNaN496.0NaNNaNNaNNaNNaNNaN4.56NaN598851NaNNaNNaNNonfictionHistoryPoliticsRaceSocial JusticeAudiobookSociologyAnti RacistAmerican HistoryAfrican American0.420.220.080.080.050.050.040.020.020.02NaN
3author1295THINGS ARE ABOUT TO GET SERIOUS FOR HARRY DRESDEN, CHICAGO’S ONLY PROFESSIONAL WIZARD, in the next entry in the #1 New York Times bestselling Dresden Files.Harry has faced terrible odds before. He has a long history of fighting enemies above his weight class. The Red Court of vampires. The fallen angels of the Order of the Blackened Denarius. The Outsiders.But this time it’s different. A being more powerful and dangerous on an order of magnitude beyond what the world has seen in a millennium is coming. And she’s bringing an army. The Last Titan has declared war on the city of Chicago, and has come to subjugate humanity, obliterating any who stand in her way.Harry’s mission is simple but impossible: Save the city by killing a Titan. And the attempt will change Harry’s life, Chicago, and the mortal world forever.---Includes the short story "Christmas Eve".HardcoverFirst Edition418.0NaNNaNNaNNaNNaNNaN4.39NaN266431NaNNaNNaNFantasyUrban FantasyFictionMagicParanormalAudiobookVampiresMysterySupernaturalFae0.410.300.080.050.040.040.030.020.020.02NaN
4author2622The Romanovs were the most successful dynasty of modern times, ruling a sixth of the world’s surface for three centuries. How did one family turn a war-ruined principality into the world’s greatest empire? And how did they lose it all? This is the intimate story of twenty tsars and tsarinas, some touched by genius, some by madness, but all inspired by holy autocracy and imperial ambition. Simon Sebag Montefiore’s gripping chronicle reveals their secret world of unlimited power and ruthless empire-building, overshadowed by palace conspiracy, family rivalries, sexual decadence and wild extravagance, with a global cast of adventurers, courtesans, revolutionaries and poets, from Ivan the Terrible to Tolstoy and Pushkin, to Bismarck, Lincoln, Queen Victoria and Lenin.HardcoverNaN784.0NaNNaNNaNNaNNaNNaN3.93NaN117721NaNNaNNaNHistoryNonfictionRussiaBiographyHistoricalRussian HistoryAudiobookPoliticsEuropean HistoryRomanovs0.420.300.090.080.040.020.010.010.010.01NaN
5author2076Shocked by a five-month arson spree that left rural Virginia reeling, Washington Post reporter Monica Hesse drove down to Accomack County to cover the trial of Charlie Smith, who pled guilty to sixty-seven counts of arson. But Charlie wasn't lighting fires alone: he had an accomplice, his girlfriend Tonya Bundick. Through her depiction of the dangerous shift that happened in their passionate relationship, Hesse brilliantly brings to life the once-thriving coastal community and its distressed inhabitants, who had already been decimated by a punishing economy before they were terrified by a string of fires they could not explain. Incorporating this drama into the long-overlooked history of arson in the United States, American Fire re-creates the anguished nights that this quiet county spent lit up in flames, mesmerizingly evoking a microcosm of rural America - a land half gutted before the fires even began.HardcoverNaN255.0NaNNaNNaNNaNNaNNaN3.79NaN136531NaNNaNNaNNonfictionTrue CrimeCrimeAudiobookHistoryMysteryAdultJournalismSociologyBiography0.450.310.070.040.040.030.020.010.010.01NaN
6author1914In a society steeped in tradition, Princess Lia’s life follows a preordained course. As First Daughter, she is expected to have the revered gift of sight—but she doesn’t—and she knows her parents are perpetrating a sham when they arrange her marriage to secure an alliance with a neighboring kingdom—to a prince she has never met.On the morning of her wedding, Lia flees to a distant village. She settles into a new life, hopeful when two mysterious and handsome strangers arrive—and unaware that one is the jilted prince and the other an assassin sent to kill her. Deception abounds, and Lia finds herself on the brink of unlocking perilous secrets—even as she finds herself falling in love. The Kiss of Deception is the first audiobook in Mary E. Pearson's Remnant ChroniclesKindle EditionNaN492.0NaNNaNNaNNaNNaNNaN3.97NaN1004091NaNNaNNaNFantasyYoung AdultRomanceYoung Adult FantasyFictionHigh FantasyAdventureMagicAudiobookDystopia0.470.210.140.050.030.030.020.020.010.01NaN
7author1818Madeleine Thien's new novel is breathtaking in scope and ambition even as it is hauntingly intimate. With the ease and skill of a master storyteller, Thien takes us inside an extended family in China, showing us the lives of two successive generations--those who lived through Mao's Cultural Revolution in the mid-twentieth century; and the children of the survivors, who became the students protesting in Tiananmen Square in 1989, in one of the most important political moments of the past century. With exquisite writing sharpened by a surprising vein of wit and sly humour, Thien has crafted unforgettable characters who are by turns flinty and headstrong, dreamy and tender, foolish and wise.At the centre of this epic tale, as capacious and mysterious as life itself, are enigmatic Sparrow, a genius composer who wishes desperately to create music yet can find truth only in silence; his mother and aunt, Big Mother Knife and Swirl, survivors with captivating singing voices and an unbreakable bond; Sparrow's ethereal cousin Zhuli, daughter of Swirl and storyteller Wen the Dreamer, who as a child witnesses the denunciation of her parents and as a young woman becomes the target of denunciations herself; and headstrong, talented Kai, best friend of Sparrow and Zhuli, and a determinedly successful musician who is a virtuoso at masking his true self until the day he can hide no longer. Here, too, is Kai's daughter, the ever-questioning mathematician Marie, who pieces together the tale of her fractured family in present-day Vancouver, seeking a fragile meaning in the layers of their collective story.With maturity and sophistication, humour and beauty, a huge heart and impressive understanding, Thien has crafted a novel that is at once beautifully intimate and grandly political, rooted in the details of daily life inside China, yet transcendent in its universality.HardcoverNaN473.0NaNNaNNaNNaNNaNNaN3.91NaN185251NaNNaNNaNFictionHistorical FictionChinaCanadaHistoricalAsiaLiterary FictionContemporaryMusicNovels0.320.300.100.060.050.040.040.030.020.02NaN
8author1478New York Times bestselling author Karin Slaughter brings back Will Trent and Sara Linton in this superb and timely thriller full of devious twists, disturbing secrets, and shocking surprises you won’t see comingA mysterious kidnappingOn a hot summer night, a scientist from the Centers for Disease Control is grabbed by unknown assailants in a shopping center parking lot. Vanished into thin air, the authorities are desperate to save the doctor. A devastating explosionOne month later, the serenity of a sunny Sunday afternoon is shattered by the boom of a ground-shaking blast—followed by another seconds later. One of Atlanta’s busiest and most important neighborhood’s has been bombed—the location of Emory University, two major hospitals, the FBI headquarters, and the CDC.A diabolical enemyMedical examiner Sara Linton and her partner Will Trent, an investigator with the Georgia Bureau of Investigation, rush to the scene—and into the heart of a deadly conspiracy that threatens to destroy thousands of innocent lives. When the assailants abduct Sara, Will goes undercover to save her and prevent a massacre—putting his own life on the line for the woman and the country he loves.HardcoverNaN446.0NaNNaNNaNNaNNaNNaN4.07NaN349941NaNNaNNaNThrillerMysteryFictionCrimeMystery ThrillerAudiobookSuspenseAdultContemporaryAdult Fiction0.240.210.160.100.090.070.070.030.020.01NaN
9author0932At long last, New York Times bestselling author Gena Showalter unveils the story of Paris, the darkest and most tormented Lord of the Underworld.Possessed by the demon of Promiscuity, immortal warrior Paris is irresistibly seductive — but his potent allure comes at a terrible price. Every night he must bed someone new, or weaken and die. And the woman he craves above all others is the one woman he'd thought was forever beyond his reach... until now.Newly possessed by the demon of Wrath, Sienna Blackstone is racked by a ruthless need to punish those around her. Yet in Paris's arms, the vulnerable beauty finds soul-searing passion and incredible peace. Until a blood feud between ancient enemies heats up.Will the battle against gods, angels and creatures of the night bind them eternally — or tear them apart?Mass Market PaperbackNaN504.0February 28, 2012publisher149NaNNaNNaN3628.744.294.62698350410.723.316.79Paranormal RomanceParanormalRomanceDemonsFantasyAngelsMythologyAdultUrban FantasySupernatural0.260.210.170.100.080.050.040.040.030.0298172.0

Last rows

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3540author2877Trouble is haunting Cassidy Blake . . . even more than usual.She (plus her ghost best friend, Jacob, of course) are in Paris, where Cass's parents are filming their TV show about the world's most haunted cities. Sure, it's fun eating croissants and seeing the Eiffel Tower, but there's true ghostly danger lurking beneath Paris, in the creepy underground Catacombs.When Cass accidentally awakens a frighteningly strong spirit, she must rely on her still-growing skills as a ghosthunter -- and turn to friends both old and new to help her unravel a mystery. But time is running out, and the spirit is only growing stronger.And if Cass fails, the force she's unleashed could haunt the city forever.#1 New York Times bestselling author Victoria Schwab returns to the spooky and heart-pounding world of City of Ghosts, delivering thrilling new adventures and an unforgettable spin on friendship. (Because sometimes, even psychic ghost best friends have secrets. . .)HardcoverNaN287.0NaNNaNNaNNaNNaNNaN4.09NaN154251NaNNaNNaNMiddle GradeFantasyParanormalAudiobookHorrorYoung AdultGhostsFictionChildrensSupernatural0.270.220.140.090.060.060.050.050.030.03NaN
3541author1187"Unexplained Fevers plucks the familiar fairy tale heroines and drops them into alternate landscapes. Unlocking them from the old stories is a way to 'rescue the other half of [their] souls.' And so Sleeping Beauty arrives at the emergency room, Red Riding Hood reaches the car dealership, and Rapunzel goes wandering in the desert - their journeys, re-imagined in this inventive collection of poems, produce other dangers, betrayals and nightmares, but also bring forth great surprise and wonder." —Rigoberto González, author of Black Blossoms Unexplained Fevers, the third full-length poetry collection from Jeannine Hall Gailey, is due for release in Spring 2013 from New Binary Press.PaperbackNaN76.0March 30, 2013publisher253NaNNaNNaN1741.794.244.8851114.810.4621.01PoetryFantasyFairy TalesFictionRetellingsNaNNaNNaNNaNNaN0.710.100.080.060.05NaNNaNNaNNaNNaN216555.0
3542author2528Adjusting to life as a world-famous cartoonist isn't easy. Terrifying deadlines, piles of junk-food wrappers under a glowing computer screen, and an ever-growing horde of pets....umm, never mind--it's pretty much the same.With characteristic wit and charm, Sarah Andersen's third collection of comics and illustrated personal essays offers a survival guide for frantic modern life: from the importance of avoiding morning people, to Internet troll defense 101, to the not-so-life-changing futility of tidying up. But when all else fails and the world around you is collapsing, make a hot chocolate, count the days until Halloween, and snuggle up next to your furry beacon of hope.PaperbackNaN112.0NaNNaNNaNNaNNaNNaN4.15NaN169191NaNNaNNaNGraphic NovelsComicsHumorNonfictionGraphic Novels ComicsAdultComedyContemporaryArtCats0.340.220.210.070.040.040.030.020.020.02NaN
3543author0929From the author of the New York Times bestseller The Dressmaker of Khair Khana comes the poignant and gripping story of a groundbreaking team of female American warriors who served alongside Special Operations soldiers on the battlefield in Afghanistan­—including Ashley White, a beloved soldier who died serving her country’s cause.In 2010, the U.S. Army Special Operations Command created Cultural Support Teams, a pilot program to put women on the battlefield alongside Green Berets and Army Rangers on sensitive missions in Afghanistan. The idea was that women could access places and people that had remained out of reach, and could build relationships—woman to woman—in ways that male soldiers in a conservative, traditional country could not. Though officially banned from combat, female soldiers could be “attached” to different teams, and for the first time, women throughout the Army heard the call to try out for this special ops program.In Ashley’s War, Gayle Tzemach Lemmon uses exhaustive firsthand reporting and a finely tuned understanding of the complexities of war to tell the story of CST-2, a unit of women hand-picked from across the Army, and the remarkable hero at its heart: 1st Lt. Ashley White, who would become the first Cultural Support Team member killed in action and the first CST remembered on the Army Special Operations Memorial Wall of Honor alongside the Army Rangers with whom she served.Transporting readers into this little-known world of fierce women bound together by valor, danger, and the desire to serve, Ashley’s War is a riveting combat narrative and a testament to the unbreakable bonds born of war.Gayle Tzemach Lemmon is a Senior Fellow at the Council on Foreign Relations and a contributor to The Atlantic’s Defense One. She is the bestselling author of The Dressmaker of Khair Khana and writes regularly for leading media outlets. A Fulbright scholar and Robert Bosch Fellow, she began reporting from conflict regions during MBA study at the Harvard Business School following nearly a decade covering politics at ABC News.HardcoverNaN292.0NaNNaNNaNNaNNaNNaN4.29NaN43951NaNNaNNaNNonfictionHistoryMilitary FictionBiographyWarFeminismWomensAudiobookBiography MemoirMemoir0.380.150.150.100.090.030.030.030.030.02NaN
3544author1618In love we find out who we want to be.In war we find out who we are.FRANCE, 1939In the quiet village of Carriveau, Vianne Mauriac says good-bye to her husband, Antoine, as he heads for the Front. She doesn’t believe that the Nazis will invade France…but invade they do, in droves of marching soldiers, in caravans of trucks and tanks, in planes that fill the skies and drop bombs upon the innocent. When a German captain requisitions Vianne’s home, she and her daughter must live with the enemy or lose everything. Without food or money or hope, as danger escalates all around them, she is forced to make one impossible choice after another to keep her family alive.Vianne’s sister, Isabelle, is a rebellious eighteen-year-old, searching for purpose with all the reckless passion of youth. While thousands of Parisians march into the unknown terrors of war, she meets Gaëtan, a partisan who believes the French can fight the Nazis from within France, and she falls in love as only the young can…completely. But when he betrays her, Isabelle joins the Resistance and never looks back, risking her life time and again to save others.With courage, grace and powerful insight, bestselling author Kristin Hannah captures the epic panorama of World War II and illuminates an intimate part of history seldom seen: the women’s war. The Nightingale tells the stories of two sisters, separated by years and experience, by ideals, passion and circumstance, each embarking on her own dangerous path toward survival, love, and freedom in German-occupied, war-torn France—a heartbreakingly beautiful novel that celebrates the resilience of the human spirit and the durability of women. It is a novel for everyone, a novel for a lifetime.HardcoverNaN440.0NaNNaNNaNNaNNaNNaN4.57NaN7876031NaNNaNNaNHistorical FictionFictionHistoricalWorld War IIWarAudiobookAdultFranceRomanceAdult Fiction0.480.210.080.050.040.040.030.020.020.02NaN
3545author1144How much is too much to love? Travis Maddox learned two things from his mother before she died: Love hard. Fight harder.Finally, the highly anticipated follow-up to the New York Times bestseller Beautiful Disaster.Can you love someone too much?Travis Maddox learned two things from his mother before she died: Love hard. Fight harder.In Walking Disaster, the life of Travis is full of fast women, underground gambling, and violence. But just when he thinks he is invincible, Abby Abernathy brings him to his knees.Every story has two sides. In Beautiful Disaster, Abby had her say. Now it’s time to see the story through Travis’s eyes.PaperbackOriginal Edition448.0NaNNaNNaNNaNNaNNaN4.19NaN1721981NaNNaNNaNRomanceNew AdultContemporaryYoung AdultContemporary RomanceCollegeFictionChick LitFightersLove0.360.240.100.070.070.060.030.020.020.02NaN
3546author2852Magneto and Professor X. Superman and Lex Luthor. Victor Vale and Eli Ever. Sydney and Serena Clarke. Great partnerships, now soured on the vine.But Marcella Riggins needs no one. Flush from her brush with death, she’s finally gained the control she’s always sought—and will use her new-found power to bring the city of Merit to its knees. She’ll do whatever it takes, collecting her own sidekicks, and leveraging the two most infamous EOs, Victor Vale and Eli Ever, against each other.With Marcella's rise, new enmities create opportunity--and the stage of Merit City will once again be set for a final, terrible reckoning. A super-powered collision of extraordinary minds and vengeful intentions—#1 New York Times bestselling author V. E. Schwab returns with the thrilling follow-up to Vicious.HardcoverNaN478.0NaNNaNNaNNaNNaNNaN4.21NaN431491NaNNaNNaNFantasyScience FictionAdultFictionUrban FantasyParanormalSuperheroesAudiobookAdult FictionYoung Adult0.430.180.110.110.040.040.030.020.020.02NaN
3547author1309Following the launch of her #1 New York Times bestselling cookbook, Magnolia Table, and seeing her family’s own sacred dishes being served on other families’ tables across the country, Joanna Gaines gained a deeper commitment to the value of food being shared. This insight inspired Joanna to get back in the kitchen and start from scratch, pushing herself beyond her comfort zone to develop new recipes for her family, and yours, to gather around.Magnolia Table, Volume 2 is filled with 145 new recipes from her own home that she shares with husband Chip and their five kids, and from the couple’s restaurant Magnolia Table, Silos Baking Co, and new coffee shop, Magnolia Press. From breakfast to dinner, plus breads, soups, and sides, Magnolia Table, Volume 2 gives readers abundant reasons to gather together.The book is beautifully photographed and filled with dishes you’ll want to bring into your own home, including: Mushroom-Gruyére Quiche, Pumpkin Cream Cheese Bread, Grilled Bruschetta Chicken, Zucchini-Squash Strata, Chicken-Pecan-Asparagus Casserole, Stuffed Pork Loin, Lemon-Lavender Tart, and Magnolia Press Chocolate Cake.HardcoverFirst Edition352.0NaNNaNNaNNaNNaNNaN3.55NaN58111NaNNaNNaNCookbooksCookingNonfictionFoodFoodieNaNNaNNaNNaNNaN0.680.140.130.050.01NaNNaNNaNNaNNaNNaN
3548author1816Bachelors, beware. For those who keep secrets and prey on the innocent, you will be exposed, with all your dirty little secrets laid bare to one and all. You have been warned...Lady Olivia Haliford has had enough. Tired of seeing women lose their reputations, futures, and sometimes even their lives to scandal while the men walk free, she is ready to take back power and stand up for women everywhere. Along with her two closest friends, she plans to start an anonymous publication dedicated to dishing the dirt and exposing the secrets of society’s most eligible bachelors. But in order to do so, she will have to make a deal with the devil...Sebastian Colver, known as the Bastard of Baker Street, is feared throughout London as the city’s most notorious gambling den owner and undisputed king of the underground. His life is nothing but darkness and danger, so he is shocked when the petite lady gracing his doorstep seems anything but frightened of him. He agrees to be a silent partner in the publishing of the Gazette if she will use her connections to sponsor his sister and launch her into Society and away from his dark world.But exposing the secrets of the rich and powerful can be dangerous. Almost as dangerous as a lady falling in love with the king of the underground.Kindle EditionNaNNaNNaNNaNNaNNaNNaNNaN4.64NaN141NaNNaNNaNHistorical RomanceNaNNaNNaNNaNNaNNaNNaNNaNNaN1.00NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
3549author0882In the thrilling, nerve-wracking finale of Ezekiel Boone’s “hair-raising” (Parade) Hatching series, the United States goes to war against the queen spiders that threaten to overtake the human race forever.The world is on the brink of apocalypse. Zero Day has come.The only thing more terrifying than millions of spiders is the realization that those spiders work as one. But among the government, there is dissent: do we try to kill all of the spiders, or do we gamble on Professor Guyer’s theory that we need to kill only the queens?For President Stephanie Pilgrim, it’s an easy answer. She’s gone as far as she can—more than two dozen American cities hit with tactical nukes, the country torn asunder—and the only answer is to believe in Professor Guyer. Unfortunately, Ben Broussard and the military men who follow him don’t agree, and Pilgrim, Guyer, and the loyal members of the government have to flee, leaving the question: what’s more dangerous, the spiders or ourselves?HardcoverNaN315.0NaNNaNNaNNaNNaNNaN3.68NaN19591NaNNaNNaNHorrorFictionThrillerScience FictionAudiobookApocalypticAdultSurvivalMystery ThrillerAction0.580.100.090.090.040.030.020.020.020.02NaN